Palm oil price forecasting model: An autoregressive distributed lag (ARDL) approach
Hamid, Mohd Fahmi Abdul; Shabri, Ani
2017-05-01
Palm oil price fluctuated without any clear trend or cyclical pattern in the last few decades. The instability of food commodities price causes it to change rapidly over time. This paper attempts to develop Autoregressive Distributed Lag (ARDL) model in modeling and forecasting the price of palm oil. In order to use ARDL as a forecasting model, this paper modifies the data structure where we only consider lagged explanatory variables to explain the variation in palm oil price. We then compare the performance of this ARDL model with a benchmark model namely ARIMA in term of their comparative forecasting accuracy. This paper also utilize ARDL bound testing approach to co-integration in examining the short run and long run relationship between palm oil price and its determinant; production, stock, and price of soybean as the substitute of palm oil and price of crude oil. The comparative forecasting accuracy suggests that ARDL model has a better forecasting accuracy compared to ARIMA.
A Dynamic Model of U.S. Sugar-Related Markets: A Cointegrated Vector Autoregression Approach
Babula, Ronald A.; Newman, Douglas; Rogowsky, Robert A.
2006-01-01
The methods of the cointegrated vector autoregression (VAR) model are applied to monthly U.S. markets for sugar and for sugar-using markets for confectionary, soft drink, and bakery products. Primarily a methods paper, we apply Johansen and Juselius' advanced procedures to these markets for perhaps the first time, with focus on achievement of a statistically adequate model through analysis of a battery of advanced statistical diagnostic tests and on exploitation of the system's cointegration ...
Empirical Vector Autoregressive Modeling
M. Ooms (Marius)
1993-01-01
textabstractChapter 2 introduces the baseline version of the VAR model, with its basic statistical assumptions that we examine in the sequel. We first check whether the variables in the VAR can be transformed to meet these assumptions. We analyze the univariate characteristics of the series.
Vector autoregressive model approach for forecasting outflow cash in Central Java
hoyyi, Abdul; Tarno; Maruddani, Di Asih I.; Rahmawati, Rita
2018-05-01
Multivariate time series model is more applied in economic and business problems as well as in other fields. Applications in economic problems one of them is the forecasting of outflow cash. This problem can be viewed globally in the sense that there is no spatial effect between regions, so the model used is the Vector Autoregressive (VAR) model. The data used in this research is data on the money supply in Bank Indonesia Semarang, Solo, Purwokerto and Tegal. The model used in this research is VAR (1), VAR (2) and VAR (3) models. Ordinary Least Square (OLS) is used to estimate parameters. The best model selection criteria use the smallest Akaike Information Criterion (AIC). The result of data analysis shows that the AIC value of VAR (1) model is equal to 42.72292, VAR (2) equals 42.69119 and VAR (3) equals 42.87662. The difference in AIC values is not significant. Based on the smallest AIC value criteria, the best model is the VAR (2) model. This model has satisfied the white noise assumption.
Multivariate Autoregressive Model Based Heart Motion Prediction Approach for Beating Heart Surgery
Directory of Open Access Journals (Sweden)
Fan Liang
2013-02-01
Full Text Available A robotic tool can enable a surgeon to conduct off-pump coronary artery graft bypass surgery on a beating heart. The robotic tool actively alleviates the relative motion between the point of interest (POI on the heart surface and the surgical tool and allows the surgeon to operate as if the heart were stationary. Since the beating heart's motion is relatively high-band, with nonlinear and nonstationary characteristics, it is difficult to follow. Thus, precise beating heart motion prediction is necessary for the tracking control procedure during the surgery. In the research presented here, we first observe that Electrocardiography (ECG signal contains the causal phase information on heart motion and non-stationary heart rate dynamic variations. Then, we investigate the relationship between ECG signal and beating heart motion using Granger Causality Analysis, which describes the feasibility of the improved prediction of heart motion. Next, we propose a nonlinear time-varying multivariate vector autoregressive (MVAR model based adaptive prediction method. In this model, the significant correlation between ECG and heart motion enables the improvement of the prediction of sharp changes in heart motion and the approximation of the motion with sufficient detail. Dual Kalman Filters (DKF estimate the states and parameters of the model, respectively. Last, we evaluate the proposed algorithm through comparative experiments using the two sets of collected vivo data.
MACROECONOMIC FORECASTING USING BAYESIAN VECTOR AUTOREGRESSIVE APPROACH
Directory of Open Access Journals (Sweden)
D. Tutberidze
2017-04-01
Full Text Available There are many arguments that can be advanced to support the forecasting activities of business entities. The underlying argument in favor of forecasting is that managerial decisions are significantly dependent on proper evaluation of future trends as market conditions are constantly changing and require a detailed analysis of future dynamics. The article discusses the importance of using reasonable macro-econometric tool by suggesting the idea of conditional forecasting through a Vector Autoregressive (VAR modeling framework. Under this framework, a macroeconomic model for Georgian economy is constructed with the few variables believed to be shaping business environment. Based on the model, forecasts of macroeconomic variables are produced, and three types of scenarios are analyzed - a baseline and two alternative ones. The results of the study provide confirmatory evidence that suggested methodology is adequately addressing the research phenomenon and can be used widely by business entities in responding their strategic and operational planning challenges. Given this set-up, it is shown empirically that Bayesian Vector Autoregressive approach provides reasonable forecasts for the variables of interest.
An Autoregressive and Distributed Lag Model Approach to Inflation in Nigeria
Directory of Open Access Journals (Sweden)
Chimere Okechukwu Iheonu
2017-03-01
Full Text Available This study scrutinized the precursors of Inflation in Nigeria between the periods 1980 to 2014. The Augmented Dickey-Fuller test was engaged to test for stationarity of the variables while the Autoregressive and Distributed lag (ARDL Model was applied to capture the affiliation between inflation and selected macroeconomic variables. Our findings revealed that there exists a long run relationship between Inflation, money supply, interest rate, GDP per capita and exchange rate in Nigeria while in the short run, money supply has a significant positive one period lag effect on Inflation and Interest Rate also has a significant negative one period lag influence on Inflation in Nigeria. Recommendations are that in the short run, monetary policies should be geared towards the control of money supply and interest rate in Nigeria in other to regulate Inflation and also, the Nigerian economy can afford to vary any of human capital development or technological advancement to boost productivity without causing inflation as GDP per capita proved insignificant in the short run.
International Nuclear Information System (INIS)
Frank, T D; Mongkolsakulvong, S
2015-01-01
In a previous study strongly nonlinear autoregressive (SNAR) models have been introduced as a generalization of the widely-used time-discrete autoregressive models that are known to apply both to Markov and non-Markovian systems. In contrast to conventional autoregressive models, SNAR models depend on process mean values. So far, only linear dependences have been studied. We consider the case in which process mean values can have a nonlinear impact on the processes under consideration. It is shown that such models describe Markov and non-Markovian many-body systems with mean field forces that exhibit a nonlinear impact on single subsystems. We exemplify that such nonlinear dependences can describe order-disorder phase transitions of time-discrete Markovian and non-Markovian many-body systems. The relevant order parameter equations are derived and issues of stability and stationarity are studied. (paper)
Chain binomial models and binomial autoregressive processes.
Weiss, Christian H; Pollett, Philip K
2012-09-01
We establish a connection between a class of chain-binomial models of use in ecology and epidemiology and binomial autoregressive (AR) processes. New results are obtained for the latter, including expressions for the lag-conditional distribution and related quantities. We focus on two types of chain-binomial model, extinction-colonization and colonization-extinction models, and present two approaches to parameter estimation. The asymptotic distributions of the resulting estimators are studied, as well as their finite-sample performance, and we give an application to real data. A connection is made with standard AR models, which also has implications for parameter estimation. © 2011, The International Biometric Society.
THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...
African Journals Online (AJOL)
The application of an allometric-autoregressive model for the quantification of growth and efficiency of feed utilization for purposes of selection for ... be of value in genetic studies. ... mass) gives a fair indication of the cumulative preweaning.
Lee, Cameron C; Sheridan, Scott C
2018-07-01
Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975-2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6-24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34-71%). Mortality responded acutely to hot summer days, peaking at 0-2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2-4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences. Copyright © 2018 Elsevier Inc. All rights reserved.
International Nuclear Information System (INIS)
Adom, Philip Kofi; Bekoe, William; Akoena, Sesi Kutri Komla
2012-01-01
In spite of the varying supply boosting efforts made by various governments to deal with the existing demand–supply gap in the electricity sector, the incessant growth in aggregate domestic electricity demand has made these efforts futile. As an objective, this paper attempts to identify the factors responsible for the historical growth trends in aggregate domestic electricity demand quantifying their effects both in the short-run and long-run periods using the ARDL Bounds cointegration approach and the sample period 1975 to 2005. In the long-run, real per capita GDP, industry efficiency, structural changes in the economy, and degree of urbanisation are identified as the main driving force behind the historical growth trend in aggregate domestic electricity demand. However, in the short-run, real per capita GDP, industry efficiency, and degree of urbanisation are the main drivers of aggregate domestic electricity demand. Industry efficiency is the only factor that drives aggregate domestic electricity demand downwards. However, the negative efficiency effect is insufficient to have outweighed the positive income, output, and demographic effects, hence the continual growth in aggregate domestic electricity demand. As a policy option, we recommend that appropriate electricity efficiency standards be implemented at the industry level. - Highlights: ► Real per capita GDP is the primary determinant of electricity demand both in the short and long-run. ► Industrial efficiency, structural changes and urbanisation rate play secondary role. ► The positive income, output, and demographic effects outweigh the negative efficiency effects.
Incorporating measurement error in n = 1 psychological autoregressive modeling
Schuurman, Noémi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive parameters. We discuss two models that take measurement error into account: An autoregressive model with a white noise term (AR+WN), and an autoregressive moving average (ARMA) model. In a simulation study we compare the parameter recovery performance of these models, and compare this performance for both a Bayesian and frequentist approach. We find that overall, the AR+WN model performs better. Furthermore, we find that for realistic (i.e., small) sample sizes, psychological research would benefit from a Bayesian approach in fitting these models. Finally, we illustrate the effect of disregarding measurement error in an AR(1) model by means of an empirical application on mood data in women. We find that, depending on the person, approximately 30–50% of the total variance was due to measurement error, and that disregarding this measurement error results in a substantial underestimation of the autoregressive parameters. PMID:26283988
Optimal Hedging with the Vector Autoregressive Model
L. Gatarek (Lukasz); S.G. Johansen (Soren)
2014-01-01
markdownabstract__Abstract__ We derive the optimal hedging ratios for a portfolio of assets driven by a Cointegrated Vector Autoregressive model with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be
Interval Forecast for Smooth Transition Autoregressive Model ...
African Journals Online (AJOL)
In this paper, we propose a simple method for constructing interval forecast for smooth transition autoregressive (STAR) model. This interval forecast is based on bootstrapping the residual error of the estimated STAR model for each forecast horizon and computing various Akaike information criterion (AIC) function. This new ...
Forecasting with periodic autoregressive time series models
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1999-01-01
textabstractThis paper is concerned with forecasting univariate seasonal time series data using periodic autoregressive models. We show how one should account for unit roots and deterministic terms when generating out-of-sample forecasts. We illustrate the models for various quarterly UK consumption
Miftahurrohmah, Brina; Iriawan, Nur; Fithriasari, Kartika
2017-06-01
Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.
New interval forecast for stationary autoregressive models ...
African Journals Online (AJOL)
In this paper, we proposed a new forecasting interval for stationary Autoregressive, AR(p) models using the Akaike information criterion (AIC) function. Ordinarily, the AIC function is used to determine the order of an AR(p) process. In this study however, AIC forecast interval compared favorably with the theoretical forecast ...
Estimation of pure autoregressive vector models for revenue series ...
African Journals Online (AJOL)
This paper aims at applying multivariate approach to Box and Jenkins univariate time series modeling to three vector series. General Autoregressive Vector Models with time varying coefficients are estimated. The first vector is a response vector, while others are predictor vectors. By matrix expansion each vector, whether ...
Circular Conditional Autoregressive Modeling of Vector Fields.
Modlin, Danny; Fuentes, Montse; Reich, Brian
2012-02-01
As hurricanes approach landfall, there are several hazards for which coastal populations must be prepared. Damaging winds, torrential rains, and tornadoes play havoc with both the coast and inland areas; but, the biggest seaside menace to life and property is the storm surge. Wind fields are used as the primary forcing for the numerical forecasts of the coastal ocean response to hurricane force winds, such as the height of the storm surge and the degree of coastal flooding. Unfortunately, developments in deterministic modeling of these forcings have been hindered by computational expenses. In this paper, we present a multivariate spatial model for vector fields, that we apply to hurricane winds. We parameterize the wind vector at each site in polar coordinates and specify a circular conditional autoregressive (CCAR) model for the vector direction, and a spatial CAR model for speed. We apply our framework for vector fields to hurricane surface wind fields for Hurricane Floyd of 1999 and compare our CCAR model to prior methods that decompose wind speed and direction into its N-S and W-E cardinal components.
Small Sample Properties of Bayesian Multivariate Autoregressive Time Series Models
Price, Larry R.
2012-01-01
The aim of this study was to compare the small sample (N = 1, 3, 5, 10, 15) performance of a Bayesian multivariate vector autoregressive (BVAR-SEM) time series model relative to frequentist power and parameter estimation bias. A multivariate autoregressive model was developed based on correlated autoregressive time series vectors of varying…
Model selection in periodic autoregressions
Ph.H.B.F. Franses (Philip Hans); R. Paap (Richard)
1994-01-01
textabstractThis paper focuses on the issue of period autoagressive time series models (PAR) selection in practice. One aspect of model selection is the choice for the appropriate PAR order. This can be of interest for the valuation of economic models. Further, the appropriate PAR order is important
Model reduction methods for vector autoregressive processes
Brüggemann, Ralf
2004-01-01
1. 1 Objective of the Study Vector autoregressive (VAR) models have become one of the dominant research tools in the analysis of macroeconomic time series during the last two decades. The great success of this modeling class started with Sims' (1980) critique of the traditional simultaneous equation models (SEM). Sims criticized the use of 'too many incredible restrictions' based on 'supposed a priori knowledge' in large scale macroeconometric models which were popular at that time. Therefore, he advo cated largely unrestricted reduced form multivariate time series models, unrestricted VAR models in particular. Ever since his influential paper these models have been employed extensively to characterize the underlying dynamics in systems of time series. In particular, tools to summarize the dynamic interaction between the system variables, such as impulse response analysis or forecast error variance decompo sitions, have been developed over the years. The econometrics of VAR models and related quantities i...
Directory of Open Access Journals (Sweden)
Shuntaro Okazaki
Full Text Available People's behaviors synchronize. It is difficult, however, to determine whether synchronized behaviors occur in a mutual direction--two individuals influencing one another--or in one direction--one individual leading the other, and what the underlying mechanism for synchronization is. To answer these questions, we hypothesized a non-leader-follower postural sway synchronization, caused by a reciprocal visuo-postural feedback system operating on pairs of individuals, and tested that hypothesis both experimentally and via simulation. In the behavioral experiment, 22 participant pairs stood face to face either 20 or 70 cm away from each other wearing glasses with or without vision blocking lenses. The existence and direction of visual information exchanged between pairs of participants were systematically manipulated. The time series data for the postural sway of these pairs were recorded and analyzed with cross correlation and causality. Results of cross correlation showed that postural sway of paired participants was synchronized, with a shorter time lag when participant pairs could see one another's head motion than when one of the participants was blindfolded. In addition, there was less of a time lag in the observed synchronization when the distance between participant pairs was smaller. As for the causality analysis, noise contribution ratio (NCR, the measure of influence using a multivariate autoregressive model, was also computed to identify the degree to which one's postural sway is explained by that of the other's and how visual information (sighted vs. blindfolded interacts with paired participants' postural sway. It was found that for synchronization to take place, it is crucial that paired participants be sighted and exert equal influence on one another by simultaneously exchanging visual information. Furthermore, a simulation for the proposed system with a wider range of visual input showed a pattern of results similar to the
Bias-correction in vector autoregressive models
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
2014-01-01
We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study......, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable...... improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find...
Testing the Conditional Mean Function of Autoregressive Conditional Duration Models
DEFF Research Database (Denmark)
Hautsch, Nikolaus
be subject to censoring structures. In an empirical study based on financial transaction data we present an application of the model to estimate conditional asset price change probabilities. Evaluating the forecasting properties of the model, it is shown that the proposed approach is a promising competitor......This paper proposes a dynamic proportional hazard (PH) model with non-specified baseline hazard for the modelling of autoregressive duration processes. A categorization of the durations allows us to reformulate the PH model as an ordered response model based on extreme value distributed errors...
Kargoll, Boris; Omidalizarandi, Mohammad; Loth, Ina; Paffenholz, Jens-André; Alkhatib, Hamza
2018-03-01
In this paper, we investigate a linear regression time series model of possibly outlier-afflicted observations and autocorrelated random deviations. This colored noise is represented by a covariance-stationary autoregressive (AR) process, in which the independent error components follow a scaled (Student's) t-distribution. This error model allows for the stochastic modeling of multiple outliers and for an adaptive robust maximum likelihood (ML) estimation of the unknown regression and AR coefficients, the scale parameter, and the degree of freedom of the t-distribution. This approach is meant to be an extension of known estimators, which tend to focus only on the regression model, or on the AR error model, or on normally distributed errors. For the purpose of ML estimation, we derive an expectation conditional maximization either algorithm, which leads to an easy-to-implement version of iteratively reweighted least squares. The estimation performance of the algorithm is evaluated via Monte Carlo simulations for a Fourier as well as a spline model in connection with AR colored noise models of different orders and with three different sampling distributions generating the white noise components. We apply the algorithm to a vibration dataset recorded by a high-accuracy, single-axis accelerometer, focusing on the evaluation of the estimated AR colored noise model.
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
2009-01-01
In this article we consider geometric ergodicity and likelihood-based inference for linear and nonlinear Poisson autoregression. In the linear case, the conditional mean is linked linearly to its past values, as well as to the observed values of the Poisson process. This also applies...... to the conditional variance, making possible interpretation as an integer-valued generalized autoregressive conditional heteroscedasticity process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and past observations. As a particular example, we consider...... an exponential autoregressive Poisson model for time series. Under geometric ergodicity, the maximum likelihood estimators are shown to be asymptotically Gaussian in the linear model. In addition, we provide a consistent estimator of their asymptotic covariance matrix. Our approach to verifying geometric...
A complex autoregressive model and application to monthly temperature forecasts
Directory of Open Access Journals (Sweden)
X. Gu
2005-11-01
Full Text Available A complex autoregressive model was established based on the mathematic derivation of the least squares for the complex number domain which is referred to as the complex least squares. The model is different from the conventional way that the real number and the imaginary number are separately calculated. An application of this new model shows a better forecast than forecasts from other conventional statistical models, in predicting monthly temperature anomalies in July at 160 meteorological stations in mainland China. The conventional statistical models include an autoregressive model, where the real number and the imaginary number are separately disposed, an autoregressive model in the real number domain, and a persistence-forecast model.
Testing and modelling autoregressive conditional heteroskedasticity of streamflow processes
Directory of Open Access Journals (Sweden)
W. Wang
2005-01-01
Full Text Available Conventional streamflow models operate under the assumption of constant variance or season-dependent variances (e.g. ARMA (AutoRegressive Moving Average models for deseasonalized streamflow series and PARMA (Periodic AutoRegressive Moving Average models for seasonal streamflow series. However, with McLeod-Li test and Engle's Lagrange Multiplier test, clear evidences are found for the existence of autoregressive conditional heteroskedasticity (i.e. the ARCH (AutoRegressive Conditional Heteroskedasticity effect, a nonlinear phenomenon of the variance behaviour, in the residual series from linear models fitted to daily and monthly streamflow processes of the upper Yellow River, China. It is shown that the major cause of the ARCH effect is the seasonal variation in variance of the residual series. However, while the seasonal variation in variance can fully explain the ARCH effect for monthly streamflow, it is only a partial explanation for daily flow. It is also shown that while the periodic autoregressive moving average model is adequate in modelling monthly flows, no model is adequate in modelling daily streamflow processes because none of the conventional time series models takes the seasonal variation in variance, as well as the ARCH effect in the residuals, into account. Therefore, an ARMA-GARCH (Generalized AutoRegressive Conditional Heteroskedasticity error model is proposed to capture the ARCH effect present in daily streamflow series, as well as to preserve seasonal variation in variance in the residuals. The ARMA-GARCH error model combines an ARMA model for modelling the mean behaviour and a GARCH model for modelling the variance behaviour of the residuals from the ARMA model. Since the GARCH model is not followed widely in statistical hydrology, the work can be a useful addition in terms of statistical modelling of daily streamflow processes for the hydrological community.
Application of autoregressive moving average model in reactor noise analysis
International Nuclear Information System (INIS)
Tran Dinh Tri
1993-01-01
The application of an autoregressive (AR) model to estimating noise measurements has achieved many successes in reactor noise analysis in the last ten years. The physical processes that take place in the nuclear reactor, however, are described by an autoregressive moving average (ARMA) model rather than by an AR model. Consequently more correct results could be obtained by applying the ARMA model instead of the AR model to reactor noise analysis. In this paper the system of the generalised Yule-Walker equations is derived from the equation of an ARMA model, then a method for its solution is given. Numerical results show the applications of the method proposed. (author)
Modelling cointegration in the vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren
2000-01-01
A survey is given of some results obtained for the cointegrated VAR. The Granger representation theorem is discussed and the notions of cointegration and common trends are defined. The statistical model for cointegrated I(1) variables is defined, and it is shown how hypotheses on the cointegratin...
Zhai, Shiyan; Song, Genxin; Qin, Yaochen; Ye, Xinyue; Lee, Jay
2017-01-01
This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June) led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.
Directory of Open Access Journals (Sweden)
Shiyan Zhai
Full Text Available This study aims to evaluate the impacts of climate change and technical progress on the wheat yield per unit area from 1970 to 2014 in Henan, the largest agricultural province in China, using an autoregressive distributed lag approach. The bounded F-test for cointegration among the model variables yielded evidence of a long-run relationship among climate change, technical progress, and the wheat yield per unit area. In the long run, agricultural machinery and fertilizer use both had significantly positive impacts on the per unit area wheat yield. A 1% increase in the aggregate quantity of fertilizer use increased the wheat yield by 0.19%. Additionally, a 1% increase in machine use increased the wheat yield by 0.21%. In contrast, precipitation during the wheat growth period (from emergence to maturity, consisting of the period from last October to June led to a decrease in the wheat yield per unit area. In the short run, the coefficient of the aggregate quantity of fertilizer used was negative. Land size had a significantly positive impact on the per unit area wheat yield in the short run. There was no significant short-run or long-run impact of temperature on the wheat yield per unit area in Henan Province. The results of our analysis suggest that climate change had a weak impact on the wheat yield, while technical progress played an important role in increasing the wheat yield per unit area. The results of this study have implications for national and local agriculture policies under climate change. To design well-targeted agriculture adaptation policies for the future and to reduce the adverse effects of climate change on the wheat yield, climate change and technical progress factors should be considered simultaneously. In addition, adaptive measures associated with technical progress should be given more attention.
Modeling Autoregressive Processes with Moving-Quantiles-Implied Nonlinearity
Directory of Open Access Journals (Sweden)
Isao Ishida
2015-01-01
Full Text Available We introduce and investigate some properties of a class of nonlinear time series models based on the moving sample quantiles in the autoregressive data generating process. We derive a test fit to detect this type of nonlinearity. Using the daily realized volatility data of Standard & Poor’s 500 (S&P 500 and several other indices, we obtained good performance using these models in an out-of-sample forecasting exercise compared with the forecasts obtained based on the usual linear heterogeneous autoregressive and other models of realized volatility.
Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)
DEFF Research Database (Denmark)
Agosto, Arianna; Cavaliere, Guiseppe; Kristensen, Dennis
We develop a class of Poisson autoregressive models with additional covariates (PARX) that can be used to model and forecast time series of counts. We establish the time series properties of the models, including conditions for stationarity and existence of moments. These results are in turn used...
Vector bilinear autoregressive time series model and its superiority ...
African Journals Online (AJOL)
In this research, a vector bilinear autoregressive time series model was proposed and used to model three revenue series (X1, X2, X3) . The “orders” of the three series were identified on the basis of the distribution of autocorrelation and partial autocorrelation functions and were used to construct the vector bilinear models.
Drought Patterns Forecasting using an Auto-Regressive Logistic Model
del Jesus, M.; Sheffield, J.; Méndez Incera, F. J.; Losada, I. J.; Espejo, A.
2014-12-01
Drought is characterized by a water deficit that may manifest across a large range of spatial and temporal scales. Drought may create important socio-economic consequences, many times of catastrophic dimensions. A quantifiable definition of drought is elusive because depending on its impacts, consequences and generation mechanism, different water deficit periods may be identified as a drought by virtue of some definitions but not by others. Droughts are linked to the water cycle and, although a climate change signal may not have emerged yet, they are also intimately linked to climate.In this work we develop an auto-regressive logistic model for drought prediction at different temporal scales that makes use of a spatially explicit framework. Our model allows to include covariates, continuous or categorical, to improve the performance of the auto-regressive component.Our approach makes use of dimensionality reduction (principal component analysis) and classification techniques (K-Means and maximum dissimilarity) to simplify the representation of complex climatic patterns, such as sea surface temperature (SST) and sea level pressure (SLP), while including information on their spatial structure, i.e. considering their spatial patterns. This procedure allows us to include in the analysis multivariate representation of complex climatic phenomena, as the El Niño-Southern Oscillation. We also explore the impact of other climate-related variables such as sun spots. The model allows to quantify the uncertainty of the forecasts and can be easily adapted to make predictions under future climatic scenarios. The framework herein presented may be extended to other applications such as flash flood analysis, or risk assessment of natural hazards.
A note on intrinsic conditional autoregressive models for disconnected graphs
Freni-Sterrantino, Anna; Ventrucci, Massimo; Rue, Haavard
2018-01-01
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
Optimal hedging with the cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Gatarek, Lukasz; Johansen, Søren
We derive the optimal hedging ratios for a portfolio of assets driven by a Coin- tegrated Vector Autoregressive model (CVAR) with general cointegration rank. Our hedge is optimal in the sense of minimum variance portfolio. We consider a model that allows for the hedges to be cointegrated with the...
A note on intrinsic conditional autoregressive models for disconnected graphs
Freni-Sterrantino, Anna
2018-05-23
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models should be defined, scaled and implemented. We show how these suggestions can be implemented in two examples, on disease mapping.
Robust bayesian analysis of an autoregressive model with ...
African Journals Online (AJOL)
In this work, robust Bayesian analysis of the Bayesian estimation of an autoregressive model with exponential innovations is performed. Using a Bayesian robustness methodology, we show that, using a suitable generalized quadratic loss, we obtain optimal Bayesian estimators of the parameters corresponding to the ...
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA; GENTON, MARC G.
2009-01-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided
Single-Index Additive Vector Autoregressive Time Series Models
LI, YEHUA
2009-09-01
We study a new class of nonlinear autoregressive models for vector time series, where the current vector depends on single-indexes defined on the past lags and the effects of different lags have an additive form. A sufficient condition is provided for stationarity of such models. We also study estimation of the proposed model using P-splines, hypothesis testing, asymptotics, selection of the order of the autoregression and of the smoothing parameters and nonlinear forecasting. We perform simulation experiments to evaluate our model in various settings. We illustrate our methodology on a climate data set and show that our model provides more accurate yearly forecasts of the El Niño phenomenon, the unusual warming of water in the Pacific Ocean. © 2009 Board of the Foundation of the Scandinavian Journal of Statistics.
Kumaraswamy autoregressive moving average models for double bounded environmental data
Bayer, Fábio Mariano; Bayer, Débora Missio; Pumi, Guilherme
2017-12-01
In this paper we introduce the Kumaraswamy autoregressive moving average models (KARMA), which is a dynamic class of models for time series taking values in the double bounded interval (a,b) following the Kumaraswamy distribution. The Kumaraswamy family of distribution is widely applied in many areas, especially hydrology and related fields. Classical examples are time series representing rates and proportions observed over time. In the proposed KARMA model, the median is modeled by a dynamic structure containing autoregressive and moving average terms, time-varying regressors, unknown parameters and a link function. We introduce the new class of models and discuss conditional maximum likelihood estimation, hypothesis testing inference, diagnostic analysis and forecasting. In particular, we provide closed-form expressions for the conditional score vector and conditional Fisher information matrix. An application to environmental real data is presented and discussed.
Likelihood inference for a nonstationary fractional autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2010-01-01
This paper discusses model-based inference in an autoregressive model for fractional processes which allows the process to be fractional of order d or d-b. Fractional differencing involves infinitely many past values and because we are interested in nonstationary processes we model the data X1......,...,X_{T} given the initial values X_{-n}, n=0,1,..., as is usually done. The initial values are not modeled but assumed to be bounded. This represents a considerable generalization relative to all previous work where it is assumed that initial values are zero. For the statistical analysis we assume...... the conditional Gaussian likelihood and for the probability analysis we also condition on initial values but assume that the errors in the autoregressive model are i.i.d. with suitable moment conditions. We analyze the conditional likelihood and its derivatives as stochastic processes in the parameters, including...
To center or not to center? Investigating inertia with a multilevel autoregressive model
Directory of Open Access Journals (Sweden)
Ellen L. Hamaker
2015-01-01
Full Text Available Whether level 1 predictors should be centered per cluster has received considerable attention in the multilevel literature. While most agree that there is no one preferred approach, it has also been argued that cluster mean centering is desirable when the within-cluster slope and the between-cluster slope are expected to deviate, and the main interest is in the within-cluster slope. However, we show in a series of simulations that if one has a multilevel autoregressive model in which the level 1 predictor is the lagged outcome variable (i.e., the outcome variable at the previous occasion, cluster mean centering will in general lead to a downward bias in the parameter estimate of the within-cluster slope (i.e., the autoregressive relationship. This is particularly relevant if the main question is whether there is on average an autoregressive effect. Nonetheless, we show that if the main interest is in estimating the effect of a level 2 predictor on the autoregressive parameter (i.e., a cross-level interaction, cluster mean centering should be preferred over other forms of centering. Hence, researchers should be clear on what is considered the main goal of their study, and base their choice of centering method on this when using a multilevel autoregressive model.
Sparse representation based image interpolation with nonlocal autoregressive modeling.
Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming
2013-04-01
Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
CICAAR - Convolutive ICA with an Auto-Regressive Inverse Model
DEFF Research Database (Denmark)
Dyrholm, Mads; Hansen, Lars Kai
2004-01-01
We invoke an auto-regressive IIR inverse model for convolutive ICA and derive expressions for the likelihood and its gradient. We argue that optimization will give a stable inverse. When there are more sensors than sources the mixing model parameters are estimated in a second step by least square...... estimation. We demonstrate the method on synthetic data and finally separate speech and music in a real room recording....
Testing exact rational expectations in cointegrated vector autoregressive models
DEFF Research Database (Denmark)
Johansen, Søren; Swensen, Anders Rygh
1999-01-01
This paper considers the testing of restrictions implied by rational expectations hypotheses in a cointegrated vector autoregressive model for I(1) variables. If the rational expectations involve one-step-ahead observations only and the coefficients are known, an explicit parameterization...... of the restrictions is found, and the maximum-likelihood estimator is derived by regression and reduced rank regression. An application is given to a present value model....
Nonlinear models for autoregressive conditional heteroskedasticity
DEFF Research Database (Denmark)
Teräsvirta, Timo
This paper contains a brief survey of nonlinear models of autore- gressive conditional heteroskedasticity. The models in question are parametric nonlinear extensions of the original model by Engle (1982). After presenting the individual models, linearity testing and parameter estimation are discu...
Mathematical model with autoregressive process for electrocardiogram signals
Evaristo, Ronaldo M.; Batista, Antonio M.; Viana, Ricardo L.; Iarosz, Kelly C.; Szezech, José D., Jr.; Godoy, Moacir F. de
2018-04-01
The cardiovascular system is composed of the heart, blood and blood vessels. Regarding the heart, cardiac conditions are determined by the electrocardiogram, that is a noninvasive medical procedure. In this work, we propose autoregressive process in a mathematical model based on coupled differential equations in order to obtain the tachograms and the electrocardiogram signals of young adults with normal heartbeats. Our results are compared with experimental tachogram by means of Poincaré plot and dentrended fluctuation analysis. We verify that the results from the model with autoregressive process show good agreement with experimental measures from tachogram generated by electrical activity of the heartbeat. With the tachogram we build the electrocardiogram by means of coupled differential equations.
Estimation of the order of an autoregressive time series: a Bayesian approach
International Nuclear Information System (INIS)
Robb, L.J.
1980-01-01
Finite-order autoregressive models for time series are often used for prediction and other inferences. Given the order of the model, the parameters of the models can be estimated by least-squares, maximum-likelihood, or Yule-Walker method. The basic problem is estimating the order of the model. The problem of autoregressive order estimation is placed in a Bayesian framework. This approach illustrates how the Bayesian method brings the numerous aspects of the problem together into a coherent structure. A joint prior probability density is proposed for the order, the partial autocorrelation coefficients, and the variance; and the marginal posterior probability distribution for the order, given the data, is obtained. It is noted that the value with maximum posterior probability is the Bayes estimate of the order with respect to a particular loss function. The asymptotic posterior distribution of the order is also given. In conclusion, Wolfer's sunspot data as well as simulated data corresponding to several autoregressive models are analyzed according to Akaike's method and the Bayesian method. Both methods are observed to perform quite well, although the Bayesian method was clearly superior, in most cases
Identification of BWR feedwater control system using autoregressive integrated model
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Andoh, Yasumasa; Yamamoto, Fumiaki; Idesawa, Masato; Itoh, Kazuo.
1983-01-01
With the view of contributing toward more reliable interpretation of noise behavior under normal operating conditions, which is essential for correct detection and/or diagnosis of incipient anomalies in nuclear power plants by noise analysis technique, studies has been undertaken of the noise behavior in a BWR feedwater control system, with use made of a multivariate autoregressive modeling technique. Noise propagation mechanisms as well as open- and closed-loop responses in the system are identified from noise data by a method in which an autoregressive integrated model is introduced. The closed-loop responses obtained with this method are compared with transient data from an actual test, and confirmed to be reliable in estimating semi-quantitative features. Other analyses performed with this model also yield results that appear most reasonable in their physical characteristics. These results have demonstrated the effectiveness of the noise analyses technique based on the autoregressive integrated model for evaluating and diagnosing the performance of feedwater control systems. (author)
Analysis of nonlinear systems using ARMA [autoregressive moving average] models
International Nuclear Information System (INIS)
Hunter, N.F. Jr.
1990-01-01
While many vibration systems exhibit primarily linear behavior, a significant percentage of the systems encountered in vibration and model testing are mildly to severely nonlinear. Analysis methods for such nonlinear systems are not yet well developed and the response of such systems is not accurately predicted by linear models. Nonlinear ARMA (autoregressive moving average) models are one method for the analysis and response prediction of nonlinear vibratory systems. In this paper we review the background of linear and nonlinear ARMA models, and illustrate the application of these models to nonlinear vibration systems. We conclude by summarizing the advantages and disadvantages of ARMA models and emphasizing prospects for future development. 14 refs., 11 figs
Bias-corrected estimation in potentially mildly explosive autoregressive models
DEFF Research Database (Denmark)
Haufmann, Hendrik; Kruse, Robinson
This paper provides a comprehensive Monte Carlo comparison of different finite-sample bias-correction methods for autoregressive processes. We consider classic situations where the process is either stationary or exhibits a unit root. Importantly, the case of mildly explosive behaviour is studied...... that the indirect inference approach oers a valuable alternative to other existing techniques. Its performance (measured by its bias and root mean squared error) is balanced and highly competitive across many different settings. A clear advantage is its applicability for mildly explosive processes. In an empirical...
REGIONAL FIRST ORDER PERIODIC AUTOREGRESSIVE MODELS FOR MONTHLY FLOWS
Directory of Open Access Journals (Sweden)
Ceyhun ÖZÇELİK
2008-01-01
Full Text Available First order periodic autoregressive models is of mostly used models in modeling of time dependency of hydrological flow processes. In these models, periodicity of the correlogram is preserved as well as time dependency of processes. However, the parameters of these models, namely, inter-monthly lag-1 autocorrelation coefficients may be often estimated erroneously from short samples, since they are statistics of high order moments. Therefore, to constitute a regional model may be a solution that can produce more reliable and decisive estimates, and derive models and model parameters in any required point of the basin considered. In this study, definitions of homogeneous region for lag-1 autocorrelation coefficients are made; five parametric and non parametric models are proposed to set regional models of lag-1 autocorrelation coefficients. Regional models are applied on 30 stream flow gauging stations in Seyhan and Ceyhan basins, and tested by criteria of relative absolute bias, simple and relative root of mean square errors.
Temporal aggregation in first order cointegrated vector autoregressive models
DEFF Research Database (Denmark)
La Cour, Lisbeth Funding; Milhøj, Anders
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline...
The cointegrated vector autoregressive model with general deterministic terms
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
2017-01-01
In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)=Z(t) Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are X 2 -distributed....
The cointegrated vector autoregressive model with general deterministic terms
DEFF Research Database (Denmark)
Johansen, Søren; Nielsen, Morten Ørregaard
In the cointegrated vector autoregression (CVAR) literature, deterministic terms have until now been analyzed on a case-by-case, or as-needed basis. We give a comprehensive unified treatment of deterministic terms in the additive model X(t)= Z(t) + Y(t), where Z(t) belongs to a large class...... of deterministic regressors and Y(t) is a zero-mean CVAR. We suggest an extended model that can be estimated by reduced rank regression and give a condition for when the additive and extended models are asymptotically equivalent, as well as an algorithm for deriving the additive model parameters from the extended...... model parameters. We derive asymptotic properties of the maximum likelihood estimators and discuss tests for rank and tests on the deterministic terms. In particular, we give conditions under which the estimators are asymptotically (mixed) Gaussian, such that associated tests are khi squared distributed....
4K Video Traffic Prediction using Seasonal Autoregressive Modeling
Directory of Open Access Journals (Sweden)
D. R. Marković
2017-06-01
Full Text Available From the perspective of average viewer, high definition video streams such as HD (High Definition and UHD (Ultra HD are increasing their internet presence year over year. This is not surprising, having in mind expansion of HD streaming services, such as YouTube, Netflix etc. Therefore, high definition video streams are starting to challenge network resource allocation with their bandwidth requirements and statistical characteristics. Need for analysis and modeling of this demanding video traffic has essential importance for better quality of service and experience support. In this paper we use an easy-to-apply statistical model for prediction of 4K video traffic. Namely, seasonal autoregressive modeling is applied in prediction of 4K video traffic, encoded with HEVC (High Efficiency Video Coding. Analysis and modeling were performed within R programming environment using over 17.000 high definition video frames. It is shown that the proposed methodology provides good accuracy in high definition video traffic modeling.
Bias-Correction in Vector Autoregressive Models: A Simulation Study
Directory of Open Access Journals (Sweden)
Tom Engsted
2014-03-01
Full Text Available We analyze the properties of various methods for bias-correcting parameter estimates in both stationary and non-stationary vector autoregressive models. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that when the model is stationary this simple bias formula compares very favorably to bootstrap bias-correction, both in terms of bias and mean squared error. In non-stationary models, the analytical bias formula performs noticeably worse than bootstrapping. Both methods yield a notable improvement over ordinary least squares. We pay special attention to the risk of pushing an otherwise stationary model into the non-stationary region of the parameter space when correcting for bias. Finally, we consider a recently proposed reduced-bias weighted least squares estimator, and we find that it compares very favorably in non-stationary models.
Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
Directory of Open Access Journals (Sweden)
A. Alexandre Trindade
2003-02-01
Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.
Least squares estimation in a simple random coefficient autoregressive model
DEFF Research Database (Denmark)
Johansen, S; Lange, T
2013-01-01
The question we discuss is whether a simple random coefficient autoregressive model with infinite variance can create the long swings, or persistence, which are observed in many macroeconomic variables. The model is defined by yt=stρyt−1+εt,t=1,…,n, where st is an i.i.d. binary variable with p...... we prove the curious result that View the MathML source. The proof applies the notion of a tail index of sums of positive random variables with infinite variance to find the order of magnitude of View the MathML source and View the MathML source and hence the limit of View the MathML source...
Incorporating measurement error in n=1 psychological autoregressive modeling
Schuurman, Noemi K.; Houtveen, Jan H.; Hamaker, Ellen L.
2015-01-01
Measurement error is omnipresent in psychological data. However, the vast majority of applications of autoregressive time series analyses in psychology do not take measurement error into account. Disregarding measurement error when it is present in the data results in a bias of the autoregressive
Bias-correction in vector autoregressive models: A simulation study
DEFF Research Database (Denmark)
Engsted, Tom; Pedersen, Thomas Quistgaard
We analyze and compare the properties of various methods for bias-correcting parameter estimates in vector autoregressions. First, we show that two analytical bias formulas from the existing literature are in fact identical. Next, based on a detailed simulation study, we show that this simple...... and easy-to-use analytical bias formula compares very favorably to the more standard but also more computer intensive bootstrap bias-correction method, both in terms of bias and mean squared error. Both methods yield a notable improvement over both OLS and a recently proposed WLS estimator. We also...... of pushing an otherwise stationary model into the non-stationary region of the parameter space during the process of correcting for bias....
The comparison study among several data transformations in autoregressive modeling
Setiyowati, Susi; Waluyo, Ramdhani Try
2015-12-01
In finance, the adjusted close of stocks are used to observe the performance of a company. The extreme prices, which may increase or decrease drastically, are often become particular concerned since it can impact to bankruptcy. As preventing action, the investors have to observe the future (forecasting) stock prices comprehensively. For that purpose, time series analysis could be one of statistical methods that can be implemented, for both stationary and non-stationary processes. Since the variability process of stocks prices tend to large and also most of time the extreme values are always exist, then it is necessary to do data transformation so that the time series models, i.e. autoregressive model, could be applied appropriately. One of popular data transformation in finance is return model, in addition to ratio of logarithm and some others Tukey ladder transformation. In this paper these transformations are applied to AR stationary models and non-stationary ARCH and GARCH models through some simulations with varying parameters. As results, this work present the suggestion table that shows transformations behavior for some condition of parameters and models. It is confirmed that the better transformation is obtained, depends on type of data distributions. In other hands, the parameter conditions term give significant influence either.
DEFF Research Database (Denmark)
Chon, K H; Cohen, R J; Holstein-Rathlou, N H
1997-01-01
A linear and nonlinear autoregressive moving average (ARMA) identification algorithm is developed for modeling time series data. The algorithm uses Laguerre expansion of kernals (LEK) to estimate Volterra-Wiener kernals. However, instead of estimating linear and nonlinear system dynamics via moving...... average models, as is the case for the Volterra-Wiener analysis, we propose an ARMA model-based approach. The proposed algorithm is essentially the same as LEK, but this algorithm is extended to include past values of the output as well. Thus, all of the advantages associated with using the Laguerre...
Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
International Nuclear Information System (INIS)
Ait Maatallah, Othman; Achuthan, Ajit; Janoyan, Kerop; Marzocca, Pier
2015-01-01
Highlights: • Developed a new recursive WSF model for 1–24 h horizon based on Hammerstein model. • Nonlinear HAR model successfully captured chaotic dynamics of wind speed time series. • Recursive WSF intrinsic error accumulation corrected by applying rotation. • Model verified for real wind speed data from two sites with different characteristics. • HAR model outperformed both ARIMA and ANN models in terms of accuracy of prediction. - Abstract: A new Wind Speed Forecasting (WSF) model, suitable for a short term 1–24 h forecast horizon, is developed by adapting Hammerstein model to an Autoregressive approach. The model is applied to real data collected for a period of three years (2004–2006) from two different sites. The performance of HAR model is evaluated by comparing its prediction with the classical Autoregressive Integrated Moving Average (ARIMA) model and a multi-layer perceptron Artificial Neural Network (ANN). Results show that the HAR model outperforms both the ARIMA model and ANN model in terms of root mean square error (RMSE), mean absolute error (MAE), and Mean Absolute Percentage Error (MAPE). When compared to the conventional models, the new HAR model can better capture various wind speed characteristics, including asymmetric (non-gaussian) wind speed distribution, non-stationary time series profile, and the chaotic dynamics. The new model is beneficial for various applications in the renewable energy area, particularly for power scheduling
vector bilinear autoregressive time series model and its superiority
African Journals Online (AJOL)
KEYWORDS: Linear time series, Autoregressive process, Autocorrelation function, Partial autocorrelation function,. Vector time .... important result on matrix algebra with respect to the spectral ..... application to covariance analysis of super-.
Likelihood inference for a fractionally cointegrated vector autoregressive model
DEFF Research Database (Denmark)
Johansen, Søren; Ørregård Nielsen, Morten
2012-01-01
such that the process X_{t} is fractional of order d and cofractional of order d-b; that is, there exist vectors ß for which ß'X_{t} is fractional of order d-b, and no other fractionality order is possible. We define the statistical model by 0inference when the true values satisfy b0¿1/2 and d0-b0......We consider model based inference in a fractionally cointegrated (or cofractional) vector autoregressive model with a restricted constant term, ¿, based on the Gaussian likelihood conditional on initial values. The model nests the I(d) VAR model. We give conditions on the parameters...... process in the parameters when errors are i.i.d. with suitable moment conditions and initial values are bounded. When the limit is deterministic this implies uniform convergence in probability of the conditional likelihood function. If the true value b0>1/2, we prove that the limit distribution of (ß...
On the maximum-entropy/autoregressive modeling of time series
Chao, B. F.
1984-01-01
The autoregressive (AR) model of a random process is interpreted in the light of the Prony's relation which relates a complex conjugate pair of poles of the AR process in the z-plane (or the z domain) on the one hand, to the complex frequency of one complex harmonic function in the time domain on the other. Thus the AR model of a time series is one that models the time series as a linear combination of complex harmonic functions, which include pure sinusoids and real exponentials as special cases. An AR model is completely determined by its z-domain pole configuration. The maximum-entropy/autogressive (ME/AR) spectrum, defined on the unit circle of the z-plane (or the frequency domain), is nothing but a convenient, but ambiguous visual representation. It is asserted that the position and shape of a spectral peak is determined by the corresponding complex frequency, and the height of the spectral peak contains little information about the complex amplitude of the complex harmonic functions.
Dealing with Multiple Solutions in Structural Vector Autoregressive Models.
Beltz, Adriene M; Molenaar, Peter C M
2016-01-01
Structural vector autoregressive models (VARs) hold great potential for psychological science, particularly for time series data analysis. They capture the magnitude, direction of influence, and temporal (lagged and contemporaneous) nature of relations among variables. Unified structural equation modeling (uSEM) is an optimal structural VAR instantiation, according to large-scale simulation studies, and it is implemented within an SEM framework. However, little is known about the uniqueness of uSEM results. Thus, the goal of this study was to investigate whether multiple solutions result from uSEM analysis and, if so, to demonstrate ways to select an optimal solution. This was accomplished with two simulated data sets, an empirical data set concerning children's dyadic play, and modifications to the group iterative multiple model estimation (GIMME) program, which implements uSEMs with group- and individual-level relations in a data-driven manner. Results revealed multiple solutions when there were large contemporaneous relations among variables. Results also verified several ways to select the correct solution when the complete solution set was generated, such as the use of cross-validation, maximum standardized residuals, and information criteria. This work has immediate and direct implications for the analysis of time series data and for the inferences drawn from those data concerning human behavior.
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbek, Anders Christian; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, implying an interpretation as an integer valued GARCH process. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model for time...
DEFF Research Database (Denmark)
Fokianos, Konstantinos; Rahbæk, Anders; Tjøstheim, Dag
This paper considers geometric ergodicity and likelihood based inference for linear and nonlinear Poisson autoregressions. In the linear case the conditional mean is linked linearly to its past values as well as the observed values of the Poisson process. This also applies to the conditional...... variance, making an interpretation as an integer valued GARCH process possible. In a nonlinear conditional Poisson model, the conditional mean is a nonlinear function of its past values and a nonlinear function of past observations. As a particular example an exponential autoregressive Poisson model...
Adaptive Autoregressive Model for Reduction of Noise in SPECT
Directory of Open Access Journals (Sweden)
Reijo Takalo
2015-01-01
Full Text Available This paper presents improved autoregressive modelling (AR to reduce noise in SPECT images. An AR filter was applied to prefilter projection images and postfilter ordered subset expectation maximisation (OSEM reconstruction images (AR-OSEM-AR method. The performance of this method was compared with filtered back projection (FBP preceded by Butterworth filtering (BW-FBP method and the OSEM reconstruction method followed by Butterworth filtering (OSEM-BW method. A mathematical cylinder phantom was used for the study. It consisted of hot and cold objects. The tests were performed using three simulated SPECT datasets. Image quality was assessed by means of the percentage contrast resolution (CR% and the full width at half maximum (FWHM of the line spread functions of the cylinders. The BW-FBP method showed the highest CR% values and the AR-OSEM-AR method gave the lowest CR% values for cold stacks. In the analysis of hot stacks, the BW-FBP method had higher CR% values than the OSEM-BW method. The BW-FBP method exhibited the lowest FWHM values for cold stacks and the AR-OSEM-AR method for hot stacks. In conclusion, the AR-OSEM-AR method is a feasible way to remove noise from SPECT images. It has good spatial resolution for hot objects.
DEFF Research Database (Denmark)
Chon, K H; Hoyer, D; Armoundas, A A
1999-01-01
In this study, we introduce a new approach for estimating linear and nonlinear stochastic autoregressive moving average (ARMA) model parameters, given a corrupt signal, using artificial recurrent neural networks. This new approach is a two-step approach in which the parameters of the deterministic...... part of the stochastic ARMA model are first estimated via a three-layer artificial neural network (deterministic estimation step) and then reestimated using the prediction error as one of the inputs to the artificial neural networks in an iterative algorithm (stochastic estimation step). The prediction...... error is obtained by subtracting the corrupt signal of the estimated ARMA model obtained via the deterministic estimation step from the system output response. We present computer simulation examples to show the efficacy of the proposed stochastic recurrent neural network approach in obtaining accurate...
Ağaç, Kübra; Koçak, Kasım; Deniz, Ali
2015-04-01
A time series approach using autoregressive model (AR), moving average model (MA) and seasonal autoregressive integrated moving average model (SARIMA) were used in this study to simulate and forecast daily PM10 concentrations in Kagithane Creek Valley, Istanbul. Hourly PM10 concentrations have been measured in Kagithane Creek Valley between 2010 and 2014 periods. Bosphorus divides the city in two parts as European and Asian parts. The historical part of the city takes place in Golden Horn. Our study area Kagithane Creek Valley is connected with this historical part. The study area is highly polluted because of its topographical structure and industrial activities. Also population density is extremely high in this site. The dispersion conditions are highly poor in this creek valley so it is necessary to calculate PM10 levels for air quality and human health. For given period there were some missing PM10 concentration values so to make an accurate calculations and to obtain exact results gap filling method was applied by Singular Spectrum Analysis (SSA). SSA is a new and efficient method for gap filling and it is an state-of-art modeling. SSA-MTM Toolkit was used for our study. SSA is considered as a noise reduction algorithm because it decomposes an original time series to trend (if exists), oscillatory and noise components by way of a singular value decomposition. The basic SSA algorithm has stages of decomposition and reconstruction. For given period daily and monthly PM10 concentrations were calculated and episodic periods are determined. Long term and short term PM10 concentrations were analyzed according to European Union (EU) standards. For simulation and forecasting of high level PM10 concentrations, meteorological data (wind speed, pressure and temperature) were used to see the relationship between daily PM10 concentrations. Fast Fourier Transformation (FFT) was also applied to the data to see the periodicity and according to these periods models were built
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J.; Engsted, T.
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
A revival of the autoregressive distributed lag model in estimating energy demand relationships
Energy Technology Data Exchange (ETDEWEB)
Bentzen, J; Engsted, T
1999-07-01
The findings in the recent energy economics literature that energy economic variables are non-stationary, have led to an implicit or explicit dismissal of the standard autoregressive distribution lag (ARDL) model in estimating energy demand relationships. However, Pesaran and Shin (1997) show that the ARDL model remains valid when the underlying variables are non-stationary, provided the variables are co-integrated. In this paper we use the ARDL approach to estimate a demand relationship for Danish residential energy consumption, and the ARDL estimates are compared to the estimates obtained using co-integration techniques and error-correction models (ECM's). It turns out that both quantitatively and qualitatively, the ARDL approach and the co-integration/ECM approach give very similar results. (au)
A Vector AutoRegressive (VAR) Approach to the Credit Channel for ...
African Journals Online (AJOL)
This paper is an attempt to determine the presence and empirical significance of monetary policy and the bank lending view of the credit channel for Mauritius, which is particularly relevant at these times. A vector autoregressive (VAR) model of order three is used to examine the monetary transmission mechanism using ...
Galka, Andreas; Siniatchkin, Michael; Stephani, Ulrich; Groening, Kristina; Wolff, Stephan; Bosch-Bayard, Jorge; Ozaki, Tohru
2010-12-01
The analysis of time series obtained by functional magnetic resonance imaging (fMRI) may be approached by fitting predictive parametric models, such as nearest-neighbor autoregressive models with exogeneous input (NNARX). As a part of the modeling procedure, it is possible to apply instantaneous linear transformations to the data. Spatial smoothing, a common preprocessing step, may be interpreted as such a transformation. The autoregressive parameters may be constrained, such that they provide a response behavior that corresponds to the canonical haemodynamic response function (HRF). We present an algorithm for estimating the parameters of the linear transformations and of the HRF within a rigorous maximum-likelihood framework. Using this approach, an optimal amount of both the spatial smoothing and the HRF can be estimated simultaneously for a given fMRI data set. An example from a motor-task experiment is discussed. It is found that, for this data set, weak, but non-zero, spatial smoothing is optimal. Furthermore, it is demonstrated that activated regions can be estimated within the maximum-likelihood framework.
Modeling money demand components in Lebanon using autoregressive models
International Nuclear Information System (INIS)
Mourad, M.
2008-01-01
This paper analyses monetary aggregate in Lebanon and its different component methodology of AR model. Thirteen variables in monthly data have been studied for the period January 1990 through December 2005. Using the Augmented Dickey-Fuller (ADF) procedure, twelve variables are integrated at order 1, thus they need the filter (1-B)) to become stationary, however the variable X 1 3,t (claims on private sector) becomes stationary with the filter (1-B)(1-B 1 2) . The ex-post forecasts have been calculated for twelve horizons and for one horizon (one-step ahead forecast). The quality of forecasts has been measured using the MAPE criterion for which the forecasts are good because the MAPE values are lower. Finally, a pursuit of this research using the cointegration approach is proposed. (author)
Self-organising mixture autoregressive model for non-stationary time series modelling.
Ni, He; Yin, Hujun
2008-12-01
Modelling non-stationary time series has been a difficult task for both parametric and nonparametric methods. One promising solution is to combine the flexibility of nonparametric models with the simplicity of parametric models. In this paper, the self-organising mixture autoregressive (SOMAR) network is adopted as a such mixture model. It breaks time series into underlying segments and at the same time fits local linear regressive models to the clusters of segments. In such a way, a global non-stationary time series is represented by a dynamic set of local linear regressive models. Neural gas is used for a more flexible structure of the mixture model. Furthermore, a new similarity measure has been introduced in the self-organising network to better quantify the similarity of time series segments. The network can be used naturally in modelling and forecasting non-stationary time series. Experiments on artificial, benchmark time series (e.g. Mackey-Glass) and real-world data (e.g. numbers of sunspots and Forex rates) are presented and the results show that the proposed SOMAR network is effective and superior to other similar approaches.
International Nuclear Information System (INIS)
Morishima, N.
1996-01-01
The multivariate autoregressive (MAR) modeling of a vector noise process is discussed in terms of the estimation of dominant noise sources in BWRs. The discussion is based on a physical approach: a transfer function model on BWR core dynamics is utilized in developing a noise model; a set of input-output relations between three system variables and twelve different noise sources is obtained. By the least-square fitting of a theoretical PSD on neutron noise to an experimental one, four kinds of dominant noise sources are selected. It is shown that some of dominant noise sources consist of two or more different noise sources and have the spectral properties of being coloured and correlated with each other. By diagonalizing the PSD matrix for dominant noise sources, we may obtain an MAR expression for a vector noise process as a response to the diagonal elements(i.e. residual noises) being white and mutually-independent. (Author)
Directory of Open Access Journals (Sweden)
Usman M. Umer
2018-06-01
Full Text Available Travel and leisure recorded a consecutive robust growth and become among the fastest economic sectors in the world. Various forecasting models are proposed by researchers that serve as an early recommendation for investors and policy makers. Numerous studies proposed distinct forecasting models to predict the dynamics of this sector and provide early recommendation for investors and policy makers. In this paper, we compare the performance of smooth transition autoregressive (STAR and linear autoregressive (AR models using monthly returns of Turkey and FTSE travel and leisure index from April 1997 to August 2016. MSCI world index used as a proxy of the overall market. The result shows that nonlinear LSTAR model cannot improve the out-of-sample forecast of linear AR model. This finding demonstrates little to be gained from using LSTAR model in the prediction of travel and leisure stock index. Keywords: Nonlinear time-series, Out-of-sample forecasting, Smooth transition autoregressive, Travel and leisure
Directory of Open Access Journals (Sweden)
Mohammad Ghahremanzadeh
2014-06-01
Full Text Available Agriculture as one of the major economic sectors of Iran, has an important role in Gross Domestic Production by providing about 14% of GDP. This study attempts to forecast the value of the agriculture GDP using Periodic Autoregressive model (PAR, as the new seasonal time series techniques. To address this aim, the quarterly data were collected from March 1988 to July 1989. The collected data was firstly analyzed using periodic unit root test Franses & Paap (2004. The analysis found non-periodic unit root in the seasonal data. Second, periodic seasonal behavior (Boswijk & Franses, 1996 was examined. The results showed that periodic autoregressive model fits agriculture GDP well. This makes an accurate forecast of agriculture GDP possible. Using the estimated model, the future value of quarter agricultural GDP from March 2011 to July 2012was forecasted. With consideration to the fair fit of this model with agricultural GDP, It is recommended to use periodic autoregressive model for the future studies.
Forecasting and simulating wind speed in Corsica by using an autoregressive model
International Nuclear Information System (INIS)
Poggi, P.; Muselli, M.; Notton, G.; Cristofari, C.; Louche, A.
2003-01-01
Alternative approaches for generating wind speed time series are discussed. The method utilized involves the use of an autoregressive process model. The model has been applied to three Mediterranean sites in Corsica and has been used to generate 3-hourly synthetic time series for these considered sites. The synthetic time series have been examined to determine their ability to preserve the statistical properties of the Corsican wind speed time series. In this context, using the main statistical characteristics of the wind speed (mean, variance, probability distribution, autocorrelation function), the data simulated are compared to experimental ones in order to check whether the wind speed behavior was correctly reproduced over the studied periods. The purpose is to create a data generator in order to construct a reference year for wind systems simulation in Corsica
Statistical aspects of autoregressive-moving average models in the assessment of radon mitigation
International Nuclear Information System (INIS)
Dunn, J.E.; Henschel, D.B.
1989-01-01
Radon values, as reflected by hourly scintillation counts, seem dominated by major, pseudo-periodic, random fluctuations. This methodological paper reports a moderate degree of success in modeling these data using relatively simple autoregressive-moving average models to assess the effectiveness of radon mitigation techniques in existing housing. While accounting for the natural correlation of successive observations, familiar summary statistics such as steady state estimates, standard errors, confidence limits, and tests of hypothesis are produced. The Box-Jenkins approach is used throughout. In particular, intervention analysis provides an objective means of assessing the effectiveness of an active mitigation measure, such as a fan off/on cycle. Occasionally, failure to declare a significant intervention has suggested a means of remedial action in the data collection procedure
Bridging Economic Theory Models and the Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
2008-01-01
Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity in the econo......Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity...... are related to expectations formation, market clearing, nominal rigidities, etc. Finally, the general-partial equilibrium distinction is analyzed....
Bridging Economic Theory Models and the Cointegrated Vector Autoregressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
2008-01-01
Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity in the econo......Examples of simple economic theory models are analyzed as restrictions on the Cointegrated VAR (CVAR). This establishes a correspondence between basic economic concepts and the econometric concepts of the CVAR: The economic relations correspond to cointegrating vectors and exogeneity...... parameters of the CVAR are shown to be interpretable in terms of expectations formation, market clearing, nominal rigidities, etc. The general-partial equilibrium distinction is also discussed....
Estimation and Forecasting in Vector Autoregressive Moving Average Models for Rich Datasets
DEFF Research Database (Denmark)
Dias, Gustavo Fruet; Kapetanios, George
We address the issue of modelling and forecasting macroeconomic variables using rich datasets, by adopting the class of Vector Autoregressive Moving Average (VARMA) models. We overcome the estimation issue that arises with this class of models by implementing an iterative ordinary least squares (...
Multivariate Self-Exciting Threshold Autoregressive Models with eXogenous Input
Addo, Peter Martey
2014-01-01
This study defines a multivariate Self--Exciting Threshold Autoregressive with eXogenous input (MSETARX) models and present an estimation procedure for the parameters. The conditions for stationarity of the nonlinear MSETARX models is provided. In particular, the efficiency of an adaptive parameter estimation algorithm and LSE (least squares estimate) algorithm for this class of models is then provided via simulations.
Linear and non-linear autoregressive models for short-term wind speed forecasting
International Nuclear Information System (INIS)
Lydia, M.; Suresh Kumar, S.; Immanuel Selvakumar, A.; Edwin Prem Kumar, G.
2016-01-01
Highlights: • Models for wind speed prediction at 10-min intervals up to 1 h built on time-series wind speed data. • Four different multivariate models for wind speed built based on exogenous variables. • Non-linear models built using three data mining algorithms outperform the linear models. • Autoregressive models based on wind direction perform better than other models. - Abstract: Wind speed forecasting aids in estimating the energy produced from wind farms. The soaring energy demands of the world and minimal availability of conventional energy sources have significantly increased the role of non-conventional sources of energy like solar, wind, etc. Development of models for wind speed forecasting with higher reliability and greater accuracy is the need of the hour. In this paper, models for predicting wind speed at 10-min intervals up to 1 h have been built based on linear and non-linear autoregressive moving average models with and without external variables. The autoregressive moving average models based on wind direction and annual trends have been built using data obtained from Sotavento Galicia Plc. and autoregressive moving average models based on wind direction, wind shear and temperature have been built on data obtained from Centre for Wind Energy Technology, Chennai, India. While the parameters of the linear models are obtained using the Gauss–Newton algorithm, the non-linear autoregressive models are developed using three different data mining algorithms. The accuracy of the models has been measured using three performance metrics namely, the Mean Absolute Error, Root Mean Squared Error and Mean Absolute Percentage Error.
de Vries, S O; Fidler, Vaclav; Kuipers, Wietze D; Hunink, Maria G M
1998-01-01
The purpose of this study was to develop a model that predicts the outcome of supervised exercise for intermittent claudication. The authors present an example of the use of autoregressive logistic regression for modeling observed longitudinal data. Data were collected from 329 participants in a
Insurance-growth nexus in Ghana: An autoregressive distributed lag bounds cointegration approach
Directory of Open Access Journals (Sweden)
Abdul Latif Alhassan
2014-12-01
Full Text Available This paper examines the long-run causal relationship between insurance penetration and economic growth in Ghana from 1990 to 2010. Using the autoregressive distributed lag (ARDL bounds approach to cointegration by Pesaran et al. (1996, 2001, the study finds a long-run positive relationship between insurance penetration and economic growth which implies that funds mobilized from insurance business have a long run impact on economic growth. A unidirectional causality was found to run from aggregate insurance penetration, life and non-life insurance penetration to economic growth to support the ‘supply-leading’ hypothesis. The findings have implications for insurance market development in Ghana.
Directory of Open Access Journals (Sweden)
Ernest Kissi
2018-03-01
Full Text Available Prices of construction resources keep on fluctuating due to unstable economic situations that have been experienced over the years. Clients knowledge of their financial commitments toward their intended project remains the basis for their final decision. The use of construction tender price index provides a realistic estimate at the early stage of the project. Tender price index (TPI is influenced by various economic factors, hence there are several statistical techniques that have been employed in forecasting. Some of these include regression, time series, vector error correction among others. However, in recent times the integrated modelling approach is gaining popularity due to its ability to give powerful predictive accuracy. Thus, in line with this assumption, the aim of this study is to apply autoregressive integrated moving average with exogenous variables (ARIMAX in modelling TPI. The results showed that ARIMAX model has a better predictive ability than the use of the single approach. The study further confirms the earlier position of previous research of the need to use the integrated model technique in forecasting TPI. This model will assist practitioners to forecast the future values of tender price index. Although the study focuses on the Ghanaian economy, the findings can be broadly applicable to other developing countries which share similar economic characteristics.
Non-linear auto-regressive models for cross-frequency coupling in neural time series
Tallot, Lucille; Grabot, Laetitia; Doyère, Valérie; Grenier, Yves; Gramfort, Alexandre
2017-01-01
We address the issue of reliably detecting and quantifying cross-frequency coupling (CFC) in neural time series. Based on non-linear auto-regressive models, the proposed method provides a generative and parametric model of the time-varying spectral content of the signals. As this method models the entire spectrum simultaneously, it avoids the pitfalls related to incorrect filtering or the use of the Hilbert transform on wide-band signals. As the model is probabilistic, it also provides a score of the model “goodness of fit” via the likelihood, enabling easy and legitimate model selection and parameter comparison; this data-driven feature is unique to our model-based approach. Using three datasets obtained with invasive neurophysiological recordings in humans and rodents, we demonstrate that these models are able to replicate previous results obtained with other metrics, but also reveal new insights such as the influence of the amplitude of the slow oscillation. Using simulations, we demonstrate that our parametric method can reveal neural couplings with shorter signals than non-parametric methods. We also show how the likelihood can be used to find optimal filtering parameters, suggesting new properties on the spectrum of the driving signal, but also to estimate the optimal delay between the coupled signals, enabling a directionality estimation in the coupling. PMID:29227989
Directory of Open Access Journals (Sweden)
Helen Higgs
2014-03-01
Full Text Available This paper models the price and income elasticity of retail finance in Australia using aggregate quarterly data and an autoregressive distributed lag (ARDL approach. We particularly focus on the impact of the global financial crisis (GFC from 2007 onwards on retail finance demand and analyse four submarkets (period analysed in brackets: owneroccupied housing loans (Sep 1985–June 2010, term loans (for motor vehicles, household goods and debt consolidation, etc. (Dec 1988–Jun 2010, credit card loans (Mar 1990–Jun 2010, and margin loans (Sep 2000–Jun 2010. Other than the indicator lending rates and annual full-time earnings respectively used as proxies for the price and income effects, we specify a large number of other variables as demand factors, particularly reflecting the value of the asset for which retail finance demand is derived. These variously include the yield on indexed bonds as a proxy for inflation expectations, median housing prices, consumer sentiment indices as measures of consumer confidence, motor vehicle and retail trade sales, housing debt-to-housing assets as a measure of leverage, the proportion of protected margin lending, the available credit limit on credit cards, and the All Ordinaries Index. In the long run, we find significant price elasticities only for term loans and margin loans, and significant income elasticities of demand for housing loans, term loans and margin loans. We also find that the GFC only significantly affected the longrun demand for term loans and margin loans. In the short run, we find that the GFC has had a significant effect on the price elasticity of demand for term loans and margin loans. Expected inflation is also a key factor affecting retail finance demand. Overall, most of the submarkets in the analysis indicate that retail finance demand is certainly price inelastic but more income elastic than conventionally thought.
Dynamics analysis of a boiling water reactor based on multivariable autoregressive modeling
International Nuclear Information System (INIS)
Oguma, Ritsuo; Matsubara, Kunihiko
1980-01-01
The establishment of the highly reliable mathematical model for the dynamic characteristics of a reactor is indispensable for the achievement of safe operation in reactor plants. The authors have tried to model the dynamic characteristics of a reactor based on the identification technique, taking the JPDR (Japan Power Demonstration Reactor) as the object, as one of the technical studies for diagnosing BWR anomaly, and employed the multivariable autoregressive modeling (MAR method) as one of the useful methods for forwarding the analysis. In this paper, the outline of the system analysis by MAR modeling is explained, and the identification experiments and their analysis results performed in the phase 4 of the power increase test of the JPDR are described. The authors evaluated the results of identification based on only reactor noises, making reference to the results of identification in the case of exciting the system by applying artificial irregular disturbance, in order to clarify the extent in which the modeling is possible by reactor noises only. However, some difficulties were encountered. The largest problem is the one concerning the separation and identification of the noise sources exciting the variables from the dynamic characteristics among the variables. If the effective technique can be obtained to this problem, the approach by the identification technique based on the probability model might be a powerful tool in the field of reactor noise analysis and the development of diagnosis technics. (Wakatsuki, Y.)
Experimental designs for autoregressive models applied to industrial maintenance
International Nuclear Information System (INIS)
Amo-Salas, M.; López-Fidalgo, J.; Pedregal, D.J.
2015-01-01
Some time series applications require data which are either expensive or technically difficult to obtain. In such cases scheduling the points in time at which the information should be collected is of paramount importance in order to optimize the resources available. In this paper time series models are studied from a new perspective, consisting in the use of Optimal Experimental Design setup to obtain the best times to take measurements, with the principal aim of saving costs or discarding useless information. The model and the covariance function are expressed in an explicit form to apply the usual techniques of Optimal Experimental Design. Optimal designs for various approaches are computed and their efficiencies are compared. The methods working in an application of industrial maintenance of a critical piece of equipment at a petrochemical plant are shown. This simple model allows explicit calculations in order to show openly the procedure to find the correlation structure, needed for computing the optimal experimental design. In this sense the techniques used in this paper to compute optimal designs may be transferred to other situations following the ideas of the paper, but taking into account the increasing difficulty of the procedure for more complex models. - Highlights: • Optimal experimental design theory is applied to AR models to reduce costs. • The first observation has an important impact on any optimal design. • Either the lack of precision or small starting observations claim for large times. • Reasonable optimal times were obtained relaxing slightly the efficiency. • Optimal designs were computed in a predictive maintenance context
Chin, Wen Cheong; Lee, Min Cherng; Yap, Grace Lee Ching
2016-01-01
High frequency financial data modelling has become one of the important research areas in the field of financial econometrics. However, the possible structural break in volatile financial time series often trigger inconsistency issue in volatility estimation. In this study, we propose a structural break heavy-tailed heterogeneous autoregressive (HAR) volatility econometric model with the enhancement of jump-robust estimators. The breakpoints in the volatility are captured by dummy variables after the detection by Bai-Perron sequential multi breakpoints procedure. In order to further deal with possible abrupt jump in the volatility, the jump-robust volatility estimators are composed by using the nearest neighbor truncation approach, namely the minimum and median realized volatility. Under the structural break improvements in both the models and volatility estimators, the empirical findings show that the modified HAR model provides the best performing in-sample and out-of-sample forecast evaluations as compared with the standard HAR models. Accurate volatility forecasts have direct influential to the application of risk management and investment portfolio analysis.
Medium- and Long-term Prediction of LOD Change by the Leap-step Autoregressive Model
Wang, Qijie
2015-08-01
The accuracy of medium- and long-term prediction of length of day (LOD) change base on combined least-square and autoregressive (LS+AR) deteriorates gradually. Leap-step autoregressive (LSAR) model can significantly reduce the edge effect of the observation sequence. Especially, LSAR model greatly improves the resolution of signals’ low-frequency components. Therefore, it can improve the efficiency of prediction. In this work, LSAR is used to forecast the LOD change. The LOD series from EOP 08 C04 provided by IERS is modeled by both the LSAR and AR models. The results of the two models are analyzed and compared. When the prediction length is between 10-30 days, the accuracy improvement is less than 10%. When the prediction length amounts to above 30 day, the accuracy improved obviously, with the maximum being around 19%. The results show that the LSAR model has higher prediction accuracy and stability in medium- and long-term prediction.
Lee, Duncan; Rushworth, Alastair; Sahu, Sujit K
2014-06-01
Estimation of the long-term health effects of air pollution is a challenging task, especially when modeling spatial small-area disease incidence data in an ecological study design. The challenge comes from the unobserved underlying spatial autocorrelation structure in these data, which is accounted for using random effects modeled by a globally smooth conditional autoregressive model. These smooth random effects confound the effects of air pollution, which are also globally smooth. To avoid this collinearity a Bayesian localized conditional autoregressive model is developed for the random effects. This localized model is flexible spatially, in the sense that it is not only able to model areas of spatial smoothness, but also it is able to capture step changes in the random effects surface. This methodological development allows us to improve the estimation performance of the covariate effects, compared to using traditional conditional auto-regressive models. These results are established using a simulation study, and are then illustrated with our motivating study on air pollution and respiratory ill health in Greater Glasgow, Scotland in 2011. The model shows substantial health effects of particulate matter air pollution and nitrogen dioxide, whose effects have been consistently attenuated by the currently available globally smooth models. © 2014, The Authors Biometrics published by Wiley Periodicals, Inc. on behalf of International Biometric Society.
Medium- and Long-term Prediction of LOD Change with the Leap-step Autoregressive Model
Liu, Q. B.; Wang, Q. J.; Lei, M. F.
2015-09-01
It is known that the accuracies of medium- and long-term prediction of changes of length of day (LOD) based on the combined least-square and autoregressive (LS+AR) decrease gradually. The leap-step autoregressive (LSAR) model is more accurate and stable in medium- and long-term prediction, therefore it is used to forecast the LOD changes in this work. Then the LOD series from EOP 08 C04 provided by IERS (International Earth Rotation and Reference Systems Service) is used to compare the effectiveness of the LSAR and traditional AR methods. The predicted series resulted from the two models show that the prediction accuracy with the LSAR model is better than that from AR model in medium- and long-term prediction.
Directory of Open Access Journals (Sweden)
Fei Jin
2013-05-01
Full Text Available This paper studies the generalized spatial two stage least squares (GS2SLS estimation of spatial autoregressive models with autoregressive disturbances when there are endogenous regressors with many valid instruments. Using many instruments may improve the efficiency of estimators asymptotically, but the bias might be large in finite samples, making the inference inaccurate. We consider the case that the number of instruments K increases with, but at a rate slower than, the sample size, and derive the approximate mean square errors (MSE that account for the trade-offs between the bias and variance, for both the GS2SLS estimator and a bias-corrected GS2SLS estimator. A criterion function for the optimal K selection can be based on the approximate MSEs. Monte Carlo experiments are provided to show the performance of our procedure of choosing K.
Genetic risk prediction using a spatial autoregressive model with adaptive lasso.
Wen, Yalu; Shen, Xiaoxi; Lu, Qing
2018-05-31
With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative. Copyright © 2018 John Wiley & Sons, Ltd.
Thresholds and Smooth Transitions in Vector Autoregressive Models
DEFF Research Database (Denmark)
Hubrich, Kirstin; Teräsvirta, Timo
This survey focuses on two families of nonlinear vector time series models, the family of Vector Threshold Regression models and that of Vector Smooth Transition Regression models. These two model classes contain incomplete models in the sense that strongly exogeneous variables are allowed in the...
Directory of Open Access Journals (Sweden)
Sun Zhangzhen
2012-08-01
Full Text Available In this paper, an improved weighted least squares (WLS, together with autoregressive (AR model, is proposed to improve prediction accuracy of earth rotation parameters(ERP. Four weighting schemes are developed and the optimal power e for determination of the weight elements is studied. The results show that the improved WLS-AR model can improve the ERP prediction accuracy effectively, and for different prediction intervals of ERP, different weight scheme should be chosen.
A Mixture Innovation Heterogeneous Autoregressive Model for Structural Breaks and Long Memory
DEFF Research Database (Denmark)
Nonejad, Nima
We propose a flexible model to describe nonlinearities and long-range dependence in time series dynamics. Our model is an extension of the heterogeneous autoregressive model. Structural breaks occur through mixture distributions in state innovations of linear Gaussian state space models. Monte...... Carlo simulations evaluate the properties of the estimation procedures. Results show that the proposed model is viable and flexible for purposes of forecasting volatility. Model uncertainty is accounted for by employing Bayesian model averaging. Bayesian model averaging provides very competitive...... forecasts compared to any single model specification. It provides further improvements when we average over nonlinear specifications....
Properties of autoregressive model in reactor noise analysis, 1
International Nuclear Information System (INIS)
Yamada, Sumasu; Kishida, Kuniharu; Bekki, Keisuke.
1987-01-01
Under appropriate conditions, stochastic processes are described by the ARMA model, however, the AR model is popularly used in reactor noise analysis. Hence, the properties of AR model as an approximate representation of the ARMA model should be made clear. Here, convergence of AR-parameters and PSD of AR model were studied through numerical analysis on specific examples such as the neutron noise in subcritical reactors, and it was found that : (1) The convergence of AR-parameters and AR model PSD is governed by the ''zero nearest to the unit circle in the complex plane'' (μ -1 ,|μ| M . (3) The AR model of the neutron noise of subcritical reactors needs a large model order because of an ARMA-zero very close to unity corresponding to the decay constant of the 6-th group of delayed neutron precursors. (4) In applying AR model for system identification, much attention has to be paid to a priori unknown error as an approximate representation of the ARMA model in addition to the statistical errors. (author)
DEFECT MONITORING IN IRON CASTING USING RESIDUES OF AUTOREGRESSIVE MODELS
Directory of Open Access Journals (Sweden)
Vanusa Andrea Casarin
2013-06-01
Full Text Available The purpose of this study is to monitor the index of general waste irons forecasting nodular and gray using the residues originated from the methodology Box & Jenkins by means of X-bar and R control charts. Search is to find a general class of model ARIMA (p, d, q but as data have autocorrelation is found to the number of residues which allowed the application of charts. The found model was the model SARIMA (0,1,1(0,1,1 . In step of checking the stability of the model was found that some comments are out of control due to temperature and chemical composition.
Bušs, Ginters
2009-01-01
Bayesian inference requires an analyst to set priors. Setting the right prior is crucial for precise forecasts. This paper analyzes how optimal prior changes when an economy is hit by a recession. For this task, an autoregressive distributed lag (ADL) model is chosen. The results show that a sharp economic slowdown changes the optimal prior in two directions. First, it changes the structure of the optimal weight prior, setting smaller weight on the lagged dependent variable compared to varia...
Temporal Aggregation in First Order Cointegrated Vector Autoregressive models
DEFF Research Database (Denmark)
Milhøj, Anders; la Cour, Lisbeth Funding
2011-01-01
with the frequency of the data. We also introduce a graphical representation that will prove useful as an additional informational tool for deciding the appropriate cointegration rank of a model. In two examples based on models of time series of different grades of gasoline, we demonstrate the usefulness of our...
An application of the Autoregressive Conditional Poisson (ACP) model
CSIR Research Space (South Africa)
Holloway, Jennifer P
2010-11-01
Full Text Available When modelling count data that comes in the form of a time series, the static Poisson regression and standard time series models are often not appropriate. A current study therefore involves the evaluation of several observation-driven and parameter...
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2012-01-01
optimized is based on penalized maximum likelihood, with exponential forgetting of past observations. MSAR models are then employed for one-step-ahead point forecasting of 10 min resolution time series of wind power at two large offshore wind farms. They are favourably compared against persistence......Wind power production data at temporal resolutions of a few minutes exhibit successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour...... and autoregressive models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....
Zhou, Tony; Dickson, Jennifer L; Geoffrey Chase, J
2018-01-01
Continuous glucose monitoring (CGM) devices have been effective in managing diabetes and offer potential benefits for use in the intensive care unit (ICU). Use of CGM devices in the ICU has been limited, primarily due to the higher point accuracy errors over currently used traditional intermittent blood glucose (BG) measures. General models of CGM errors, including drift and random errors, are lacking, but would enable better design of protocols to utilize these devices. This article presents an autoregressive (AR) based modeling method that separately characterizes the drift and random noise of the GlySure CGM sensor (GlySure Limited, Oxfordshire, UK). Clinical sensor data (n = 33) and reference measurements were used to generate 2 AR models to describe sensor drift and noise. These models were used to generate 100 Monte Carlo simulations based on reference blood glucose measurements. These were then compared to the original CGM clinical data using mean absolute relative difference (MARD) and a Trend Compass. The point accuracy MARD was very similar between simulated and clinical data (9.6% vs 9.9%). A Trend Compass was used to assess trend accuracy, and found simulated and clinical sensor profiles were similar (simulated trend index 11.4° vs clinical trend index 10.9°). The model and method accurately represents cohort sensor behavior over patients, providing a general modeling approach to any such sensor by separately characterizing each type of error that can arise in the data. Overall, it enables better protocol design based on accurate expected CGM sensor behavior, as well as enabling the analysis of what level of each type of sensor error would be necessary to obtain desired glycemic control safety and performance with a given protocol.
Autcha Araveeporn
2013-01-01
This paper compares a Least-Squared Random Coefficient Autoregressive (RCA) model with a Least-Squared RCA model based on Autocorrelated Errors (RCA-AR). We looked at only the first order models, denoted RCA(1) and RCA(1)-AR(1). The efficiency of the Least-Squared method was checked by applying the models to Brownian motion and Wiener process, and the efficiency followed closely the asymptotic properties of a normal distribution. In a simulation study, we compared the performance of RCA(1) an...
Directory of Open Access Journals (Sweden)
MEHDI AMIAN
2013-10-01
Full Text Available Functional near infrared spectroscopy (fNIRS is a technique that is used for noninvasive measurement of the oxyhemoglobin (HbO2 and deoxyhemoglobin (HHb concentrations in the brain tissue. Since the ratio of the concentration of these two agents is correlated with the neuronal activity, fNIRS can be used for the monitoring and quantifying the cortical activity. The portability of fNIRS makes it a good candidate for studies involving subject's movement. The fNIRS measurements, however, are sensitive to artifacts generated by subject's head motion. This makes fNIRS signals less effective in such applications. In this paper, the autoregressive moving average (ARMA modeling of the fNIRS signal is proposed for state-space representation of the signal which is then fed to the Kalman filter for estimating the motionless signal from motion corrupted signal. Results are compared to the autoregressive model (AR based approach, which has been done previously, and show that the ARMA models outperform AR models. We attribute it to the richer structure, containing more terms indeed, of ARMA than AR. We show that the signal to noise ratio (SNR is about 2 dB higher for ARMA based method.
A Two-Factor Autoregressive Moving Average Model Based on Fuzzy Fluctuation Logical Relationships
Directory of Open Access Journals (Sweden)
Shuang Guan
2017-10-01
Full Text Available Many of the existing autoregressive moving average (ARMA forecast models are based on one main factor. In this paper, we proposed a new two-factor first-order ARMA forecast model based on fuzzy fluctuation logical relationships of both a main factor and a secondary factor of a historical training time series. Firstly, we generated a fluctuation time series (FTS for two factors by calculating the difference of each data point with its previous day, then finding the absolute means of the two FTSs. We then constructed a fuzzy fluctuation time series (FFTS according to the defined linguistic sets. The next step was establishing fuzzy fluctuation logical relation groups (FFLRGs for a two-factor first-order autoregressive (AR(1 model and forecasting the training data with the AR(1 model. Then we built FFLRGs for a two-factor first-order autoregressive moving average (ARMA(1,m model. Lastly, we forecasted test data with the ARMA(1,m model. To illustrate the performance of our model, we used real Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX and Dow Jones datasets as a secondary factor to forecast TAIEX. The experiment results indicate that the proposed two-factor fluctuation ARMA method outperformed the one-factor method based on real historic data. The secondary factor may have some effects on the main factor and thereby impact the forecasting results. Using fuzzified fluctuations rather than fuzzified real data could avoid the influence of extreme values in historic data, which performs negatively while forecasting. To verify the accuracy and effectiveness of the model, we also employed our method to forecast the Shanghai Stock Exchange Composite Index (SHSECI from 2001 to 2015 and the international gold price from 2000 to 2010.
Noncausal Bayesian Vector Autoregression
DEFF Research Database (Denmark)
Lanne, Markku; Luoto, Jani
We propose a Bayesian inferential procedure for the noncausal vector autoregressive (VAR) model that is capable of capturing nonlinearities and incorporating effects of missing variables. In particular, we devise a fast and reliable posterior simulator that yields the predictive distribution...
Recognition of NEMP and LEMP signals based on auto-regression model and artificial neutral network
International Nuclear Information System (INIS)
Li Peng; Song Lijun; Han Chao; Zheng Yi; Cao Baofeng; Li Xiaoqiang; Zhang Xueqin; Liang Rui
2010-01-01
Auto-regression (AR) model, one power spectrum estimation method of stationary random signals, and artificial neutral network were adopted to recognize nuclear and lightning electromagnetic pulses. Self-correlation function and Burg algorithms were used to acquire the AR model coefficients as eigenvalues, and BP artificial neural network was introduced as the classifier with different numbers of hidden layers and hidden layer nodes. The results show that AR model is effective in those signals, feature extraction, and the Burg algorithm is more effective than the self-correlation function algorithm. (authors)
A representation theory for a class of vector autoregressive models for fractional processes
DEFF Research Database (Denmark)
Johansen, Søren
2008-01-01
Based on an idea of Granger (1986), we analyze a new vector autoregressive model defined from the fractional lag operator 1-(1-L)^{d}. We first derive conditions in terms of the coefficients for the model to generate processes which are fractional of order zero. We then show that if there is a un...... root, the model generates a fractional process X(t) of order d, d>0, for which there are vectors ß so that ß'X(t) is fractional of order d-b, 0...
Lohani, A. K.; Kumar, Rakesh; Singh, R. D.
2012-06-01
SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.
Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models
Energy Technology Data Exchange (ETDEWEB)
Pappas, S.S. [Department of Information and Communication Systems Engineering, University of the Aegean, Karlovassi, 83 200 Samos (Greece); Ekonomou, L.; Chatzarakis, G.E. [Department of Electrical Engineering Educators, ASPETE - School of Pedagogical and Technological Education, N. Heraklion, 141 21 Athens (Greece); Karamousantas, D.C. [Technological Educational Institute of Kalamata, Antikalamos, 24100 Kalamata (Greece); Katsikas, S.K. [Department of Technology Education and Digital Systems, University of Piraeus, 150 Androutsou Srt., 18 532 Piraeus (Greece); Liatsis, P. [Division of Electrical Electronic and Information Engineering, School of Engineering and Mathematical Sciences, Information and Biomedical Engineering Centre, City University, Northampton Square, London EC1V 0HB (United Kingdom)
2008-09-15
This study addresses the problem of modeling the electricity demand loads in Greece. The provided actual load data is deseasonilized and an AutoRegressive Moving Average (ARMA) model is fitted on the data off-line, using the Akaike Corrected Information Criterion (AICC). The developed model fits the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on-line/adaptive modeling is required. In both cases and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise are performed. The produced results indicate that the proposed method, which is based on the multi-model partitioning theory, tackles successfully the studied problem. For validation purposes the produced results are compared with three other established order selection criteria, namely AICC, Akaike's Information Criterion (AIC) and Schwarz's Bayesian Information Criterion (BIC). The developed model could be useful in the studies that concern electricity consumption and electricity prices forecasts. (author)
Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model.
Bringmann, Laura F; Ferrer, Emilio; Hamaker, Ellen L; Borsboom, Denny; Tuerlinckx, Francis
2018-01-01
Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male's emotion dynamics over time, but not in the female's-which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.
Autoregressive Model with Partial Forgetting within Rao-Blackwellized Particle Filter
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Hofman, Radek
2012-01-01
Roč. 41, č. 5 (2012), s. 582-589 ISSN 0361-0918 R&D Projects: GA MV VG20102013018; GA ČR GA102/08/0567 Grant - others:ČVUT(CZ) SGS 10/099/OHK3/1T/16 Institutional research plan: CEZ:AV0Z10750506 Keywords : Bayesian methods * Particle filters * Recursive estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.295, year: 2012 http://library.utia.cas.cz/separaty/2012/AS/dedecius-autoregressive model with partial forgetting within rao-blackwellized particle filter.pdf
Bildirici, Melike; Ersin, Özgür Ömer
2018-01-01
The study aims to combine the autoregressive distributed lag (ARDL) cointegration framework with smooth transition autoregressive (STAR)-type nonlinear econometric models for causal inference. Further, the proposed STAR distributed lag (STARDL) models offer new insights in terms of modeling nonlinearity in the long- and short-run relations between analyzed variables. The STARDL method allows modeling and testing nonlinearity in the short-run and long-run parameters or both in the short- and long-run relations. To this aim, the relation between CO 2 emissions and economic growth rates in the USA is investigated for the 1800-2014 period, which is one of the largest data sets available. The proposed hybrid models are the logistic, exponential, and second-order logistic smooth transition autoregressive distributed lag (LSTARDL, ESTARDL, and LSTAR2DL) models combine the STAR framework with nonlinear ARDL-type cointegration to augment the linear ARDL approach with smooth transitional nonlinearity. The proposed models provide a new approach to the relevant econometrics and environmental economics literature. Our results indicated the presence of asymmetric long-run and short-run relations between the analyzed variables that are from the GDP towards CO 2 emissions. By the use of newly proposed STARDL models, the results are in favor of important differences in terms of the response of CO 2 emissions in regimes 1 and 2 for the estimated LSTAR2DL and LSTARDL models.
Directory of Open Access Journals (Sweden)
Rahul Tripathi
2014-01-01
Full Text Available Forecasting of rice area, production, and productivity of Odisha was made from the historical data of 1950-51 to 2008-09 by using univariate autoregressive integrated moving average (ARIMA models and was compared with the forecasted all Indian data. The autoregressive (p and moving average (q parameters were identified based on the significant spikes in the plots of partial autocorrelation function (PACF and autocorrelation function (ACF of the different time series. ARIMA (2, 1, 0 model was found suitable for all Indian rice productivity and production, whereas ARIMA (1, 1, 1 was best fitted for forecasting of rice productivity and production in Odisha. Prediction was made for the immediate next three years, that is, 2007-08, 2008-09, and 2009-10, using the best fitted ARIMA models based on minimum value of the selection criterion, that is, Akaike information criteria (AIC and Schwarz-Bayesian information criteria (SBC. The performances of models were validated by comparing with percentage deviation from the actual values and mean absolute percent error (MAPE, which was found to be 0.61 and 2.99% for the area under rice in Odisha and India, respectively. Similarly for prediction of rice production and productivity in Odisha and India, the MAPE was found to be less than 6%.
Shi, Jinfei; Zhu, Songqing; Chen, Ruwen
2017-12-01
An order selection method based on multiple stepwise regressions is proposed for General Expression of Nonlinear Autoregressive model which converts the model order problem into the variable selection of multiple linear regression equation. The partial autocorrelation function is adopted to define the linear term in GNAR model. The result is set as the initial model, and then the nonlinear terms are introduced gradually. Statistics are chosen to study the improvements of both the new introduced and originally existed variables for the model characteristics, which are adopted to determine the model variables to retain or eliminate. So the optimal model is obtained through data fitting effect measurement or significance test. The simulation and classic time-series data experiment results show that the method proposed is simple, reliable and can be applied to practical engineering.
Schliep, E. M.; Gelfand, A. E.; Holland, D. M.
2015-12-01
There is considerable demand for accurate air quality information in human health analyses. The sparsity of ground monitoring stations across the United States motivates the need for advanced statistical models to predict air quality metrics, such as PM2.5, at unobserved sites. Remote sensing technologies have the potential to expand our knowledge of PM2.5 spatial patterns beyond what we can predict from current PM2.5 monitoring networks. Data from satellites have an additional advantage in not requiring extensive emission inventories necessary for most atmospheric models that have been used in earlier data fusion models for air pollution. Statistical models combining monitoring station data with satellite-obtained aerosol optical thickness (AOT), also referred to as aerosol optical depth (AOD), have been proposed in the literature with varying levels of success in predicting PM2.5. The benefit of using AOT is that satellites provide complete gridded spatial coverage. However, the challenges involved with using it in fusion models are (1) the correlation between the two data sources varies both in time and in space, (2) the data sources are temporally and spatially misaligned, and (3) there is extensive missingness in the monitoring data and also in the satellite data due to cloud cover. We propose a hierarchical autoregressive spatially varying coefficients model to jointly model the two data sources, which addresses the foregoing challenges. Additionally, we offer formal model comparison for competing models in terms of model fit and out of sample prediction of PM2.5. The models are applied to daily observations of PM2.5 and AOT in the summer months of 2013 across the conterminous United States. Most notably, during this time period, we find small in-sample improvement incorporating AOT into our autoregressive model but little out-of-sample predictive improvement.
Krishnan, M.; Bhowmik, B.; Hazra, B.; Pakrashi, V.
2018-02-01
In this paper, a novel baseline free approach for continuous online damage detection of multi degree of freedom vibrating structures using Recursive Principal Component Analysis (RPCA) in conjunction with Time Varying Auto-Regressive Modeling (TVAR) is proposed. In this method, the acceleration data is used to obtain recursive proper orthogonal components online using rank-one perturbation method, followed by TVAR modeling of the first transformed response, to detect the change in the dynamic behavior of the vibrating system from its pristine state to contiguous linear/non-linear-states that indicate damage. Most of the works available in the literature deal with algorithms that require windowing of the gathered data owing to their data-driven nature which renders them ineffective for online implementation. Algorithms focussed on mathematically consistent recursive techniques in a rigorous theoretical framework of structural damage detection is missing, which motivates the development of the present framework that is amenable for online implementation which could be utilized along with suite experimental and numerical investigations. The RPCA algorithm iterates the eigenvector and eigenvalue estimates for sample covariance matrices and new data point at each successive time instants, using the rank-one perturbation method. TVAR modeling on the principal component explaining maximum variance is utilized and the damage is identified by tracking the TVAR coefficients. This eliminates the need for offline post processing and facilitates online damage detection especially when applied to streaming data without requiring any baseline data. Numerical simulations performed on a 5-dof nonlinear system under white noise excitation and El Centro (also known as 1940 Imperial Valley earthquake) excitation, for different damage scenarios, demonstrate the robustness of the proposed algorithm. The method is further validated on results obtained from case studies involving
International Nuclear Information System (INIS)
McCall, K C; Jeraj, R
2007-01-01
A new approach to the problem of modelling and predicting respiration motion has been implemented. This is a dual-component model, which describes the respiration motion as a non-periodic time series superimposed onto a periodic waveform. A periodic autoregressive moving average algorithm has been used to define a mathematical model of the periodic and non-periodic components of the respiration motion. The periodic components of the motion were found by projecting multiple inhale-exhale cycles onto a common subspace. The component of the respiration signal that is left after removing this periodicity is a partially autocorrelated time series and was modelled as an autoregressive moving average (ARMA) process. The accuracy of the periodic ARMA model with respect to fluctuation in amplitude and variation in length of cycles has been assessed. A respiration phantom was developed to simulate the inter-cycle variations seen in free-breathing and coached respiration patterns. At ±14% variability in cycle length and maximum amplitude of motion, the prediction errors were 4.8% of the total motion extent for a 0.5 s ahead prediction, and 9.4% at 1.0 s lag. The prediction errors increased to 11.6% at 0.5 s and 21.6% at 1.0 s when the respiration pattern had ±34% variations in both these parameters. Our results have shown that the accuracy of the periodic ARMA model is more strongly dependent on the variations in cycle length than the amplitude of the respiration cycles
Remaining Useful Life Prediction of Gas Turbine Engine using Autoregressive Model
Directory of Open Access Journals (Sweden)
Ahsan Shazaib
2017-01-01
Full Text Available Gas turbine (GT engines are known for their high availability and reliability and are extensively used for power generation, marine and aero-applications. Maintenance of such complex machines should be done proactively to reduce cost and sustain high availability of the GT. The aim of this paper is to explore the use of autoregressive (AR models to predict remaining useful life (RUL of a GT engine. The Turbofan Engine data from NASA benchmark data repository is used as case study. The parametric investigation is performed to check on any effect of changing model parameter on modelling accuracy. Results shows that a single sensory data cannot accurately predict RUL of GT and further research need to be carried out by incorporating multi-sensory data. Furthermore, the predictions made using AR model seems to give highly pessimistic values for RUL of GT.
International Nuclear Information System (INIS)
Benmouiza, Khalil; Cheknane, Ali
2013-01-01
Highlights: • An unsupervised clustering algorithm with a neural network model was explored. • The forecasting results of solar radiation time series and the comparison of their performance was simulated. • A new method was proposed combining k-means algorithm and NAR network to provide better prediction results. - Abstract: In this paper, we review our work for forecasting hourly global horizontal solar radiation based on the combination of unsupervised k-means clustering algorithm and artificial neural networks (ANN). k-Means algorithm focused on extracting useful information from the data with the aim of modeling the time series behavior and find patterns of the input space by clustering the data. On the other hand, nonlinear autoregressive (NAR) neural networks are powerful computational models for modeling and forecasting nonlinear time series. Taking the advantage of both methods, a new method was proposed combining k-means algorithm and NAR network to provide better forecasting results
Sin, Kuek Jia; Cheong, Chin Wen; Hooi, Tan Siow
2017-04-01
This study aims to investigate the crude oil volatility using a two components autoregressive conditional heteroscedasticity (ARCH) model with the inclusion of abrupt jump feature. The model is able to capture abrupt jumps, news impact, clustering volatility, long persistence volatility and heavy-tailed distributed error which are commonly observed in the crude oil time series. For the empirical study, we have selected the WTI crude oil index from year 2000 to 2016. The results found that by including the multiple-abrupt jumps in ARCH model, there are significant improvements of estimation evaluations as compared with the standard ARCH models. The outcomes of this study can provide useful information for risk management and portfolio analysis in the crude oil markets.
International Nuclear Information System (INIS)
Wang, Jianzhou; Hu, Jianming
2015-01-01
With the increasing importance of wind power as a component of power systems, the problems induced by the stochastic and intermittent nature of wind speed have compelled system operators and researchers to search for more reliable techniques to forecast wind speed. This paper proposes a combination model for probabilistic short-term wind speed forecasting. In this proposed hybrid approach, EWT (Empirical Wavelet Transform) is employed to extract meaningful information from a wind speed series by designing an appropriate wavelet filter bank. The GPR (Gaussian Process Regression) model is utilized to combine independent forecasts generated by various forecasting engines (ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM)) in a nonlinear way rather than the commonly used linear way. The proposed approach provides more probabilistic information for wind speed predictions besides improving the forecasting accuracy for single-value predictions. The effectiveness of the proposed approach is demonstrated with wind speed data from two wind farms in China. The results indicate that the individual forecasting engines do not consistently forecast short-term wind speed for the two sites, and the proposed combination method can generate a more reliable and accurate forecast. - Highlights: • The proposed approach can make probabilistic modeling for wind speed series. • The proposed approach adapts to the time-varying characteristic of the wind speed. • The hybrid approach can extract the meaningful components from the wind speed series. • The proposed method can generate adaptive, reliable and more accurate forecasting results. • The proposed model combines four independent forecasting engines in a nonlinear way.
Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar
2017-02-15
Effective connectivity (EC) analysis of neuronal groups using fMRI delivers insights about functional-integration. However, fMRI signal has low-temporal resolution due to down-sampling and indirectly measures underlying neuronal activity. The aim is to address above issues for more reliable EC estimates. This paper proposes use of autoregressive hidden Markov model with missing data (AR-HMM-md) in dynamically multi-linked (DML) framework for learning EC using multiple fMRI time series. In our recent work (Dang et al., 2016), we have shown how AR-HMM-md for modelling single fMRI time series outperforms the existing methods. AR-HMM-md models unobserved neuronal activity and lost data over time as variables and estimates their values by joint optimization given fMRI observation sequence. The effectiveness in learning EC is shown using simulated experiments. Also the effects of sampling and noise are studied on EC. Moreover, classification-experiments are performed for Attention-Deficit/Hyperactivity Disorder subjects and age-matched controls for performance evaluation of real data. Using Bayesian model selection, we see that the proposed model converged to higher log-likelihood and demonstrated that group-classification can be performed with higher cross-validation accuracy of above 94% using distinctive network EC which characterizes patients vs. The full data EC obtained from DML-AR-HMM-md is more consistent with previous literature than the classical multivariate Granger causality method. The proposed architecture leads to reliable estimates of EC than the existing latent models. This framework overcomes the disadvantage of low-temporal resolution and improves cross-validation accuracy significantly due to presence of missing data variables and autoregressive process. Copyright © 2016 Elsevier B.V. All rights reserved.
Forecasting nuclear power supply with Bayesian autoregression
International Nuclear Information System (INIS)
Beck, R.; Solow, J.L.
1994-01-01
We explore the possibility of forecasting the quarterly US generation of electricity from nuclear power using a Bayesian autoregression model. In terms of forecasting accuracy, this approach compares favorably with both the Department of Energy's current forecasting methodology and their more recent efforts using ARIMA models, and it is extremely easy and inexpensive to implement. (author)
International Nuclear Information System (INIS)
Che Jinxing; Wang Jianzhou
2010-01-01
In this paper, we present the use of different mathematical models to forecast electricity price under deregulated power. A successful prediction tool of electricity price can help both power producers and consumers plan their bidding strategies. Inspired by that the support vector regression (SVR) model, with the ε-insensitive loss function, admits of the residual within the boundary values of ε-tube, we propose a hybrid model that combines both SVR and Auto-regressive integrated moving average (ARIMA) models to take advantage of the unique strength of SVR and ARIMA models in nonlinear and linear modeling, which is called SVRARIMA. A nonlinear analysis of the time-series indicates the convenience of nonlinear modeling, the SVR is applied to capture the nonlinear patterns. ARIMA models have been successfully applied in solving the residuals regression estimation problems. The experimental results demonstrate that the model proposed outperforms the existing neural-network approaches, the traditional ARIMA models and other hybrid models based on the root mean square error and mean absolute percentage error.
Bekti, Rokhana Dwi; Nurhadiyanti, Gita; Irwansyah, Edy
2014-10-01
The diarrhea case pattern information, especially for toddler, is very important. It is used to show the distribution of diarrhea in every region, relationship among that locations, and regional economic characteristic or environmental behavior. So, this research uses spatial pattern to perform them. This method includes: Moran's I, Spatial Autoregressive Models (SAR), and Local Indicator of Spatial Autocorrelation (LISA). It uses sample from 23 sub districts of Bekasi Regency, West Java, Indonesia. Diarrhea case, regional economic, and environmental behavior of households have a spatial relationship among sub district. SAR shows that the percentage of Regional Gross Domestic Product is significantly effect on diarrhea at α = 10%. Therefore illiteracy and health center facilities are significant at α = 5%. With LISA test, sub districts in southern Bekasi have high dependencies with Cikarang Selatan, Serang Baru, and Setu. This research also builds development application that is based on java and R to support data analysis.
Autoregressive-model-based missing value estimation for DNA microarray time series data.
Choong, Miew Keen; Charbit, Maurice; Yan, Hong
2009-01-01
Missing value estimation is important in DNA microarray data analysis. A number of algorithms have been developed to solve this problem, but they have several limitations. Most existing algorithms are not able to deal with the situation where a particular time point (column) of the data is missing entirely. In this paper, we present an autoregressive-model-based missing value estimation method (ARLSimpute) that takes into account the dynamic property of microarray temporal data and the local similarity structures in the data. ARLSimpute is especially effective for the situation where a particular time point contains many missing values or where the entire time point is missing. Experiment results suggest that our proposed algorithm is an accurate missing value estimator in comparison with other imputation methods on simulated as well as real microarray time series datasets.
DEFF Research Database (Denmark)
Thomsen, C E; Rosenfalck, A; Nørregaard Christensen, K
1991-01-01
The brain activity electroencephalogram (EEG) was recorded from 30 healthy women scheduled for hysterectomy. The patients were anaesthetized with isoflurane, halothane or etomidate/fentanyl. A multiparametric method was used for extraction of amplitude and frequency information from the EEG....... The method applied autoregressive modelling of the signal, segmented in 2 s fixed intervals. The features from the EEG segments were used for learning and for classification. The learning process was unsupervised and hierarchical clustering analysis was used to construct a learning set of EEG amplitude......-frequency patterns for each of the three anaesthetic drugs. These EEG patterns were assigned to a colour code corresponding to similar clinical states. A common learning set could be used for all patients anaesthetized with the same drug. The classification process could be performed on-line and the results were...
International Nuclear Information System (INIS)
Oguma, Ritsuo
1980-01-01
In the HBWR (Halden Boiling Water Reactor), there exists a resonant power oscillation with period about 0.04 Hz at power levels higher than about 9.5 MWt. While the resonant oscillation in not so large as to affect the normal reactor operation, it is significant, from the viewpoint of reactor diagnosis, to grasp its characteristics and find the cause. Noise analysis based on the autoregressive (AR) modeling technique has been made to reveal the driving source for this oscillation which led to the suggestion that it is attributed to the dynamic interference of heat exchange process between two parallel-connected steam transformers against the reactor. The present study demonstrates that the method used here is highly effective for tracing back to a noise source inducing the variation of quantities in a system, and also applicable to problems of reactor noise analysis and diagnosis. (author)
Evaluation of the autoregression time-series model for analysis of a noisy signal
International Nuclear Information System (INIS)
Allen, J.W.
1977-01-01
The autoregression (AR) time-series model of a continuous noisy signal was statistically evaluated to determine quantitatively the uncertainties of the model order, the model parameters, and the model's power spectral density (PSD). The result of such a statistical evaluation enables an experimenter to decide whether an AR model can adequately represent a continuous noisy signal and be consistent with the signal's frequency spectrum, and whether it can be used for on-line monitoring. Although evaluations of other types of signals have been reported in the literature, no direct reference has been found to AR model's uncertainties for continuous noisy signals; yet the evaluation is necessary to decide the usefulness of AR models of typical reactor signals (e.g., neutron detector output or thermocouple output) and the potential of AR models for on-line monitoring applications. AR and other time-series models for noisy data representation are being investigated by others since such models require fewer parameters than the traditional PSD model. For this study, the AR model was selected for its simplicity and conduciveness to uncertainty analysis, and controlled laboratory bench signals were used for continuous noisy data. (author)
Benbenishty, Rami; Astor, Ron Avi; Roziner, Ilan; Wrabel, Stephani L.
2016-01-01
The present study explores the causal link between school climate, school violence, and a school's general academic performance over time using a school-level, cross-lagged panel autoregressive modeling design. We hypothesized that reductions in school violence and climate improvement would lead to schools' overall improved academic performance.…
Forecasting Nord Pool day-ahead prices with an autoregressive model
International Nuclear Information System (INIS)
Kristiansen, Tarjei
2012-01-01
This paper presents a model to forecast Nord Pool hourly day-ahead prices. The model is based on but reduced in terms of estimation parameters (from 24 sets to 1) and modified to include Nordic demand and Danish wind power as exogenous variables. We model prices across all hours in the analysis period rather than across each single hour of 24 hours. By applying three model variants on Nord Pool data, we achieve a weekly mean absolute percentage error (WMAE) of around 6–7% and an hourly mean absolute percentage error (MAPE) ranging from 8% to 11%. Out of sample results yields a WMAE and an hourly MAPE of around 5%. The models enable analysts and traders to forecast hourly day-ahead prices accurately. Moreover, the models are relatively straightforward and user-friendly to implement. They can be set up in any trading organization. - Highlights: ► Forecasting Nord Pool day-ahead prices with an autoregressive model. ► The model is based on but with the set of parameters reduced from 24 to 1. ► The model includes Nordic demand and Danish wind power as exogenous variables. ► Hourly mean absolute percentage error ranges from 8% to 11%. ► Out of sample results yields a WMAE and an hourly MAPE of around 5%.
DEFF Research Database (Denmark)
Li, Chunjian; Andersen, Søren Vang
2007-01-01
We propose two blind system identification methods that exploit the underlying dynamics of non-Gaussian signals. The two signal models to be identified are: an Auto-Regressive (AR) model driven by a discrete-state Hidden Markov process, and the same model whose output is perturbed by white Gaussi...... outputs. The signal models are general and suitable to numerous important signals, such as speech signals and base-band communication signals. Applications to speech analysis and blind channel equalization are given to exemplify the efficiency of the new methods....
Assessment and prediction of air quality using fuzzy logic and autoregressive models
Carbajal-Hernández, José Juan; Sánchez-Fernández, Luis P.; Carrasco-Ochoa, Jesús A.; Martínez-Trinidad, José Fco.
2012-12-01
In recent years, artificial intelligence methods have been used for the treatment of environmental problems. This work, presents two models for assessment and prediction of air quality. First, we develop a new computational model for air quality assessment in order to evaluate toxic compounds that can harm sensitive people in urban areas, affecting their normal activities. In this model we propose to use a Sigma operator to statistically asses air quality parameters using their historical data information and determining their negative impact in air quality based on toxicity limits, frequency average and deviations of toxicological tests. We also introduce a fuzzy inference system to perform parameter classification using a reasoning process and integrating them in an air quality index describing the pollution levels in five stages: excellent, good, regular, bad and danger, respectively. The second model proposed in this work predicts air quality concentrations using an autoregressive model, providing a predicted air quality index based on the fuzzy inference system previously developed. Using data from Mexico City Atmospheric Monitoring System, we perform a comparison among air quality indices developed for environmental agencies and similar models. Our results show that our models are an appropriate tool for assessing site pollution and for providing guidance to improve contingency actions in urban areas.
DEFF Research Database (Denmark)
Kock, Anders Bredahl
2016-01-01
We show that the adaptive Lasso is oracle efficient in stationary and nonstationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Directory of Open Access Journals (Sweden)
Carlos Quispe
2013-04-01
Full Text Available El Niño connects globally climate, ecosystems and socio-economic activities. Since 1980 this event has been tried to be predicted, but until now the statistical and dynamical models are insuffi cient. Thus, the objective of the present work was to explore using an autoregressive moving average model the effect of El Niño over the sea surface temperature (TSM off the Peruvian coast. The work involved 5 stages: identifi cation, estimation, diagnostic checking, forecasting and validation. Simple and partial autocorrelation functions (FAC and FACP were used to identify and reformulate the orders of the model parameters, as well as Akaike information criterium (AIC and Schwarz criterium (SC for the selection of the best models during the diagnostic checking. Among the main results the models ARIMA(12,0,11 were proposed, which simulated monthly conditions in agreement with the observed conditions off the Peruvian coast: cold conditions at the end of 2004, and neutral conditions at the beginning of 2005.
Nabelek, Daniel P.; Ho, K. C.
2013-06-01
The detection of shallow buried low-metal content objects using ground penetrating radar (GPR) is a challenging task. This is because these targets are right underneath the ground and the ground bounce reflection interferes with their detections. They do not create distinctive hyperbolic signatures as required by most existing GPR detection algorithms due to their special geometric shapes and low metal content. This paper proposes the use of the Autoregressive (AR) modeling method for the detection of these targets. We fit an A-scan of the GPR data to an AR model. It is found that the fitting error will be small when such a target is present and large when it is absent. The ratio of the energy in an Ascan before and after AR model fitting is used as the confidence value for detection. We also apply AR model fitting over scans and utilize the fitting residual energies over several scans to form a feature vector for improving the detections. Using the data collected from a government test site, the proposed method can improve the detection of this kind of targets by 30% compared to the pre-screener, at a false alarm rate of 0.002/m2.
A time series model: First-order integer-valued autoregressive (INAR(1))
Simarmata, D. M.; Novkaniza, F.; Widyaningsih, Y.
2017-07-01
Nonnegative integer-valued time series arises in many applications. A time series model: first-order Integer-valued AutoRegressive (INAR(1)) is constructed by binomial thinning operator to model nonnegative integer-valued time series. INAR (1) depends on one period from the process before. The parameter of the model can be estimated by Conditional Least Squares (CLS). Specification of INAR(1) is following the specification of (AR(1)). Forecasting in INAR(1) uses median or Bayesian forecasting methodology. Median forecasting methodology obtains integer s, which is cumulative density function (CDF) until s, is more than or equal to 0.5. Bayesian forecasting methodology forecasts h-step-ahead of generating the parameter of the model and parameter of innovation term using Adaptive Rejection Metropolis Sampling within Gibbs sampling (ARMS), then finding the least integer s, where CDF until s is more than or equal to u . u is a value taken from the Uniform(0,1) distribution. INAR(1) is applied on pneumonia case in Penjaringan, Jakarta Utara, January 2008 until April 2016 monthly.
Modeling Polio Data Using the First Order Non-Negative Integer-Valued Autoregressive, INAR(1), Model
Vazifedan, Turaj; Shitan, Mahendran
Time series data may consists of counts, such as the number of road accidents, the number of patients in a certain hospital, the number of customers waiting for service at a certain time and etc. When the value of the observations are large it is usual to use Gaussian Autoregressive Moving Average (ARMA) process to model the time series. However if the observed counts are small, it is not appropriate to use ARMA process to model the observed phenomenon. In such cases we need to model the time series data by using Non-Negative Integer valued Autoregressive (INAR) process. The modeling of counts data is based on the binomial thinning operator. In this paper we illustrate the modeling of counts data using the monthly number of Poliomyelitis data in United States between January 1970 until December 1983. We applied the AR(1), Poisson regression model and INAR(1) model and the suitability of these models were assessed by using the Index of Agreement(I.A.). We found that INAR(1) model is more appropriate in the sense it had a better I.A. and it is natural since the data are counts.
Energy Technology Data Exchange (ETDEWEB)
Ciftcioglu, O.; Hoogenboom, J.E.; Dam, H. van
1988-01-01
Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality.
International Nuclear Information System (INIS)
Ciftcioglu, O.
1988-01-01
Studies on the multivariate autoregressive (MAR) analysis are carried out for the choice of the parameters for modelling the data obtained from various sensors optimally. Accordingly, the roles of the parameters on the analysis results are identified and the related ambiguities are reduced. Experimental investigations are carried out by means of synthesized reactor noise-like data obtained from a digital simulator providing simulated stochastic signals of an operating nuclear reactor so that the simulator constitutes a favourable tool for the present studies aimed. As the system is well defined with its known structure, precise comparison of the MAR analysis results with the true values is performed. With the help of the information gained through the studies carried out, conditions to be taken care of for optimal signal processing in MAR modelling are determined. Although the parameters involved are related among themselves and they have to be given different values suitable for the particular application in hand, some criteria, namely memory-time and sample length-time play an essential role in AR modelling and they are found to be applicable to each individual case commonly, for the establishment of the optimality. (author)
A Ramp Cosine Cepstrum Model for the Parameter Estimation of Autoregressive Systems at Low SNR
Directory of Open Access Journals (Sweden)
Zhu Wei-Ping
2010-01-01
Full Text Available A new cosine cepstrum model-based scheme is presented for the parameter estimation of a minimum-phase autoregressive (AR system under low levels of signal-to-noise ratio (SNR. A ramp cosine cepstrum (RCC model for the one-sided autocorrelation function (OSACF of an AR signal is first proposed by considering both white noise and periodic impulse-train excitations. Using the RCC model, a residue-based least-squares optimization technique that guarantees the stability of the system is then presented in order to estimate the AR parameters from noisy output observations. For the purpose of implementation, the discrete cosine transform, which can efficiently handle the phase unwrapping problem and offer computational advantages as compared to the discrete Fourier transform, is employed. From extensive experimentations on AR systems of different orders, it is shown that the proposed method is capable of estimating parameters accurately and consistently in comparison to some of the existing methods for the SNR levels as low as −5 dB. As a practical application of the proposed technique, simulation results are also provided for the identification of a human vocal tract system using noise-corrupted natural speech signals demonstrating a superior estimation performance in terms of the power spectral density of the synthesized speech signals.
Taghvaei, Sajjad; Jahanandish, Mohammad Hasan; Kosuge, Kazuhiro
2017-01-01
Population aging of the societies requires providing the elderly with safe and dependable assistive technologies in daily life activities. Improving the fall detection algorithms can play a major role in achieving this goal. This article proposes a real-time fall prediction algorithm based on the acquired visual data of a user with walking assistive system from a depth sensor. In the lack of a coupled dynamic model of the human and the assistive walker a hybrid "system identification-machine learning" approach is used. An autoregressive-moving-average (ARMA) model is fitted on the time-series walking data to forecast the upcoming states, and a hidden Markov model (HMM) based classifier is built on the top of the ARMA model to predict falling in the upcoming time frames. The performance of the algorithm is evaluated through experiments with four subjects including an experienced physiotherapist while using a walker robot in five different falling scenarios; namely, fall forward, fall down, fall back, fall left, and fall right. The algorithm successfully predicts the fall with a rate of 84.72%.
Liu, Zhuofu; Wang, Lin; Luo, Zhongming; Heusch, Andrew I; Cascioli, Vincenzo; McCarthy, Peter W
2015-11-01
There is a need to develop a greater understanding of temperature at the skin-seat interface during prolonged seating from the perspectives of both industrial design (comfort/discomfort) and medical care (skin ulcer formation). Here we test the concept of predicting temperature at the seat surface and skin interface during prolonged sitting (such as required from wheelchair users). As caregivers are usually busy, such a method would give them warning ahead of a problem. This paper describes a data-driven model capable of predicting thermal changes and thus having the potential to provide an early warning (15- to 25-min ahead prediction) of an impending temperature that may increase the risk for potential skin damages for those subject to enforced sitting and who have little or no sensory feedback from this area. Initially, the oscillations of the original signal are suppressed using the reconstruction strategy of empirical mode decomposition (EMD). Consequentially, the autoregressive data-driven model can be used to predict future thermal trends based on a shorter period of acquisition, which reduces the possibility of introducing human errors and artefacts associated with longer duration "enforced" sitting by volunteers. In this study, the method had a maximum predictive error of body insensitivity and disability requiring them to be immobile in seats for prolonged periods. Copyright © 2015 Tissue Viability Society. Published by Elsevier Ltd. All rights reserved.
Inflation, Exchange Rates and Interest Rates in Ghana: an Autoregressive Distributed Lag Model
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Dennis Nchor
2015-01-01
Full Text Available This paper investigates the impact of exchange rate movement and the nominal interest rate on inflation in Ghana. It also looks at the presence of the Fisher Effect and the International Fisher Effect scenarios. It makes use of an autoregressive distributed lag model and an unrestricted error correction model. Ordinary Least Squares regression methods were also employed to determine the presence of the Fischer Effect and the International Fisher Effect. The results from the study show that in the short run a percentage point increase in the level of depreciation of the Ghana cedi leads to an increase in the rate of inflation by 0.20%. A percentage point increase in the level of nominal interest rates however results in a decrease in inflation by 0.98%. Inflation increases by 1.33% for every percentage point increase in the nominal interest rate in the long run. An increase in inflation on the other hand increases the nominal interest rate by 0.51% which demonstrates the partial Fisher effect. A 1% increase in the interest rate differential leads to a depreciation of the Ghana cedi by approximately 1% which indicates the full International Fisher effect.
Kammerdiner, Alla; Xanthopoulos, Petros; Pardalos, Panos M.
2007-11-01
In this chapter a potential problem with application of the Granger-causality based on the simple vector autoregressive (VAR) modeling to EEG data is investigated. Although some initial studies tested whether the data support the stationarity assumption of VAR, the stability of the estimated model is rarely (if ever) been verified. In fact, in cases when the stability condition is violated the process may exhibit a random walk like behavior or even be explosive. The problem is illustrated by an example.
Noise source analysis of nuclear ship Mutsu plant using multivariate autoregressive model
International Nuclear Information System (INIS)
Hayashi, K.; Shimazaki, J.; Shinohara, Y.
1996-01-01
The present study is concerned with the noise sources in N.S. Mutsu reactor plant. The noise experiments on the Mutsu plant were performed in order to investigate the plant dynamics and the effect of sea condition and and ship motion on the plant. The reactor noise signals as well as the ship motion signals were analyzed by a multivariable autoregressive (MAR) modeling method to clarify the noise sources in the reactor plant. It was confirmed from the analysis results that most of the plant variables were affected mainly by a horizontal component of the ship motion, that is the sway, through vibrations of the plant structures. Furthermore, the effect of ship motion on the reactor power was evaluated through the analysis of wave components extracted by a geometrical transform method. It was concluded that the amplitude of the reactor power oscillation was about 0.15% in normal sea condition, which was small enough for safe operation of the reactor plant. (authors)
Investigating Spatial Interdependence in E-Bike Choice Using Spatially Autoregressive Model
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Chengcheng Xu
2017-08-01
Full Text Available Increased attention has been given to promoting e-bike usage in recent years. However, the research gap still exists in understanding the effects of spatial interdependence on e-bike choice. This study investigated how spatial interdependence affected the e-bike choice. The Moran’s I statistic test showed that spatial interdependence exists in e-bike choice at aggregated level. Bayesian spatial autoregressive logistic analyses were then used to investigate the spatial interdependence at individual level. Separate models were developed for commuting and non-commuting trips. The factors affecting e-bike choice are different between commuting and non-commuting trips. Spatial interdependence exists at both origin and destination sides of commuting and non-commuting trips. Travellers are more likely to choose e-bikes if their neighbours at the trip origin and destination also travel by e-bikes. And the magnitude of this spatial interdependence is different across various traffic analysis zones. The results suggest that, without considering spatial interdependence, the traditional methods may have biased estimation results and make systematic forecasting errors.
Yu, Bing; Shu, Wenjun; Cao, Can
2018-05-01
A novel modeling method for aircraft engine using nonlinear autoregressive exogenous (NARX) models based on wavelet neural networks is proposed. The identification principle and process based on wavelet neural networks are studied, and the modeling scheme based on NARX is proposed. Then, the time series data sets from three types of aircraft engines are utilized to build the corresponding NARX models, and these NARX models are validated by the simulation. The results show that all the best NARX models can capture the original aircraft engine's dynamic characteristic well with the high accuracy. For every type of engine, the relative identification errors of its best NARX model and the component level model are no more than 3.5 % and most of them are within 1 %.
Pemodelan Markov Switching Autoregressive
Ariyani, Fiqria Devi; Warsito, Budi; Yasin, Hasbi
2014-01-01
Transition from depreciation to appreciation of exchange rate is one of regime switching that ignored by classic time series model, such as ARIMA, ARCH, or GARCH. Therefore, economic variables are modeled by Markov Switching Autoregressive (MSAR) which consider the regime switching. MLE is not applicable to parameters estimation because regime is an unobservable variable. So that filtering and smoothing process are applied to see the regime probabilities of observation. Using this model, tran...
Prediction of high airway pressure using a non-linear autoregressive model of pulmonary mechanics.
Langdon, Ruby; Docherty, Paul D; Schranz, Christoph; Chase, J Geoffrey
2017-11-02
For mechanically ventilated patients with acute respiratory distress syndrome (ARDS), suboptimal PEEP levels can cause ventilator induced lung injury (VILI). In particular, high PEEP and high peak inspiratory pressures (PIP) can cause over distension of alveoli that is associated with VILI. However, PEEP must also be sufficient to maintain recruitment in ARDS lungs. A lung model that accurately and precisely predicts the outcome of an increase in PEEP may allow dangerous high PIP to be avoided, and reduce the incidence of VILI. Sixteen pressure-flow data sets were collected from nine mechanically ventilated ARDs patients that underwent one or more recruitment manoeuvres. A nonlinear autoregressive (NARX) model was identified on one or more adjacent PEEP steps, and extrapolated to predict PIP at 2, 4, and 6 cmH 2 O PEEP horizons. The analysis considered whether the predicted and measured PIP exceeded a threshold of 40 cmH 2 O. A direct comparison of the method was made using the first order model of pulmonary mechanics (FOM(I)). Additionally, a further, more clinically appropriate method for the FOM was tested, in which the FOM was trained on a single PEEP prior to prediction (FOM(II)). The NARX model exhibited very high sensitivity (> 0.96) in all cases, and a high specificity (> 0.88). While both FOM methods had a high specificity (> 0.96), the sensitivity was much lower, with a mean of 0.68 for FOM(I), and 0.82 for FOM(II). Clinically, false negatives are more harmful than false positives, as a high PIP may result in distension and VILI. Thus, the NARX model may be more effective than the FOM in allowing clinicians to reduce the risk of applying a PEEP that results in dangerously high airway pressures.
Fisher, Aaron J; Reeves, Jonathan W; Chi, Cyrus
2016-07-01
Expanding on recently published methods, the current study presents an approach to estimating the dynamic, regulatory effect of the parasympathetic nervous system on heart period on a moment-to-moment basis. We estimated second-to-second variation in respiratory sinus arrhythmia (RSA) in order to estimate the contemporaneous and time-lagged relationships among RSA, interbeat interval (IBI), and respiration rate via vector autoregression. Moreover, we modeled these relationships at lags of 1 s to 10 s, in order to evaluate the optimal latency for estimating dynamic RSA effects. The IBI (t) on RSA (t-n) regression parameter was extracted from individual models as an operationalization of the regulatory effect of RSA on IBI-referred to as dynamic RSA (dRSA). Dynamic RSA positively correlated with standard averages of heart rate and negatively correlated with standard averages of RSA. We propose that dRSA reflects the active downregulation of heart period by the parasympathetic nervous system and thus represents a novel metric that provides incremental validity in the measurement of autonomic cardiac control-specifically, a method by which parasympathetic regulatory effects can be measured in process. © 2016 Society for Psychophysiological Research.
Nurhaida, Subanar, Abdurakhman, Abadi, Agus Maman
2017-08-01
Seismic data is usually modelled using autoregressive processes. The aim of this paper is to find the arrival times of the seismic waves of Mt. Rinjani in Indonesia. Kitagawa algorithm's is used to detect the seismic P and S-wave. Householder transformation used in the algorithm made it effectively finding the number of change points and parameters of the autoregressive models. The results show that the use of Box-Cox transformation on the variable selection level makes the algorithm works well in detecting the change points. Furthermore, when the basic span of the subinterval is set 200 seconds and the maximum AR order is 20, there are 8 change points which occur at 1601, 2001, 7401, 7601,7801, 8001, 8201 and 9601. Finally, The P and S-wave arrival times are detected at time 1671 and 2045 respectively using a precise detection algorithm.
Generalizing smooth transition autoregressions
DEFF Research Database (Denmark)
Chini, Emilio Zanetti
We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail, with part......We introduce a variant of the smooth transition autoregression - the GSTAR model - capable to parametrize the asymmetry in the tails of the transition equation by using a particular generalization of the logistic function. A General-to-Specific modelling strategy is discussed in detail......, with particular emphasis on two different LM-type tests for the null of symmetric adjustment towards a new regime and three diagnostic tests, whose power properties are explored via Monte Carlo experiments. Four classical real datasets illustrate the empirical properties of the GSTAR, jointly to a rolling...
Wo-Chiang Lee; Joe-Ming Lee
2014-01-01
This article applies the threshold autoregressive model to investigate the relationship between bond funds’ net flow and investment risk in Taiwan. Our empirical findings show that bond funds’ investors are concerned about the investment return and neglect the investment risk. In particular, when expanding the size of the bond funds, fund investors believe that the fund cannot lose any money on investment products. In order to satisfy investors, bond fund managers only target short-term retur...
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, R.N.J.; Vries, L.B.
1986-01-01
The authors present an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Adaptive interpolation of discrete-time signals that can be modeled as autoregressive processes
Janssen, A.J.E.M.; Veldhuis, Raymond N.J.; Vries, Lodewijk B.
1986-01-01
This paper presents an adaptive algorithm for the restoration of lost sample values in discrete-time signals that can locally be described by means of autoregressive processes. The only restrictions are that the positions of the unknown samples should be known and that they should be embedded in a
Directory of Open Access Journals (Sweden)
Ryan Louise
2007-11-01
Full Text Available Abstract Background The Conditional Autoregressive (CAR model is widely used in many small-area ecological studies to analyse outcomes measured at an areal level. There has been little evaluation of the influence of different neighbourhood weight matrix structures on the amount of smoothing performed by the CAR model. We examined this issue in detail. Methods We created several neighbourhood weight matrices and applied them to a large dataset of births and birth defects in New South Wales (NSW, Australia within 198 Statistical Local Areas. Between the years 1995–2003, there were 17,595 geocoded birth defects and 770,638 geocoded birth records with available data. Spatio-temporal models were developed with data from 1995–2000 and their fit evaluated within the following time period: 2001–2003. Results We were able to create four adjacency-based weight matrices, seven distance-based weight matrices and one matrix based on similarity in terms of a key covariate (i.e. maternal age. In terms of agreement between observed and predicted relative risks, categorised in epidemiologically relevant groups, generally the distance-based matrices performed better than the adjacency-based neighbourhoods. In terms of recovering the underlying risk structure, the weight-7 model (smoothing by maternal-age 'Covariate model' was able to correctly classify 35/47 high-risk areas (sensitivity 74% with a specificity of 47%, and the 'Gravity' model had sensitivity and specificity values of 74% and 39% respectively. Conclusion We found considerable differences in the smoothing properties of the CAR model, depending on the type of neighbours specified. This in turn had an effect on the models' ability to recover the observed risk in an area. Prior to risk mapping or ecological modelling, an exploratory analysis of the neighbourhood weight matrix to guide the choice of a suitable weight matrix is recommended. Alternatively, the weight matrix can be chosen a priori
Linking Simple Economic Theory Models and the Cointegrated Vector AutoRegressive Model
DEFF Research Database (Denmark)
Møller, Niels Framroze
This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its stru....... Further fundamental extensions and advances to more sophisticated theory models, such as those related to dynamics and expectations (in the structural relations) are left for future papers......This paper attempts to clarify the connection between simple economic theory models and the approach of the Cointegrated Vector-Auto-Regressive model (CVAR). By considering (stylized) examples of simple static equilibrium models, it is illustrated in detail, how the theoretical model and its......, it is demonstrated how other controversial hypotheses such as Rational Expectations can be formulated directly as restrictions on the CVAR-parameters. A simple example of a "Neoclassical synthetic" AS-AD model is also formulated. Finally, the partial- general equilibrium distinction is related to the CVAR as well...
Directory of Open Access Journals (Sweden)
Yu-Pin Liao
2017-11-01
Full Text Available In the past few decades, demand forecasting has become relatively difficult due to rapid changes in the global environment. This research illustrates the use of the make-to-stock (MTS production strategy in order to explain how forecasting plays an essential role in business management. The linear mixed-effect (LME model has been extensively developed and is widely applied in various fields. However, no study has used the LME model for business forecasting. We suggest that the LME model be used as a tool for prediction and to overcome environment complexity. The data analysis is based on real data in an international display company, where the company needs accurate demand forecasting before adopting a MTS strategy. The forecasting result from the LME model is compared to the commonly used approaches, including the regression model, autoregressive model, times series model, and exponential smoothing model, with the results revealing that prediction performance provided by the LME model is more stable than using the other methods. Furthermore, product types in the data are regarded as a random effect in the LME model, hence demands of all types can be predicted simultaneously using a single LME model. However, some approaches require splitting the data into different type categories, and then predicting the type demand by establishing a model for each type. This feature also demonstrates the practicability of the LME model in real business operations.
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Ali Akbar Akbari
2014-08-01
Full Text Available Introduction In order to improve the quality of life of amputees, biomechatronic researchers and biomedical engineers have been trying to use a combination of various techniques to provide suitable rehabilitation systems. Diverse biomedical signals, acquired from a specialized organ or cell system, e.g., the nervous system, are the driving force for the whole system. Electromyography(EMG, as an experimental technique,is concerned with the development, recording, and analysis of myoelectric signals. EMG-based research is making progress in the development of simple, robust, user-friendly, and efficient interface devices for the amputees. Materials and Methods Prediction of muscular activity and motion patterns is a common, practical problem in prosthetic organs. Recurrent neural network (RNN models are not only applicable for the prediction of time series, but are also commonly used for the control of dynamical systems. The prediction can be assimilated to identification of a dynamic process. An architectural approach of RNN with embedded memory is Nonlinear Autoregressive Exogenous (NARX model, which seems to be suitable for dynamic system applications. Results Performance of NARX model is verified for several chaotic time series, which are applied as input for the neural network. The results showed that NARX has the potential to capture the model of nonlinear dynamic systems. The R-value and MSE are and , respectively. Conclusion EMG signals of deltoid and pectoralis major muscles are the inputs of the NARX network. It is possible to obtain EMG signals of muscles in other arm motions to predict the lost functions of the absent arm in above-elbow amputees, using NARX model.
DEFF Research Database (Denmark)
He, Changli; Kang, Jian; Terasvirta, Timo
In this paper we introduce an autoregressive model with seasonal dummy variables in which coefficients of seasonal dummies vary smoothly and deterministically over time. The error variance of the model is seasonally heteroskedastic and multiplicatively decomposed, the decomposition being similar ...... temperature series. More specifically, the idea is to find out in which way and by how much the monthly temperatures are varying over time during the period of more than 240 years, if they do. Misspecification tests are applied to the estimated model and the findings discussed....
Energy Technology Data Exchange (ETDEWEB)
Lu, Fengbin, E-mail: fblu@amss.ac.cn [Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190 (China); Qiao, Han, E-mail: qiaohan@ucas.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Wang, Shouyang, E-mail: sywang@amss.ac.cn [School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190 (China); Lai, Kin Keung, E-mail: mskklai@cityu.edu.hk [Department of Management Sciences, City University of Hong Kong (Hong Kong); Li, Yuze, E-mail: richardyz.li@mail.utoronto.ca [Department of Industrial Engineering, University of Toronto (Canada)
2017-01-15
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
International Nuclear Information System (INIS)
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor’s 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model.
Lu, Fengbin; Qiao, Han; Wang, Shouyang; Lai, Kin Keung; Li, Yuze
2017-01-01
This paper proposes a new time-varying coefficient vector autoregressions (VAR) model, in which the coefficient is a linear function of dynamic lagged correlation. The proposed model allows for flexibility in choices of dynamic correlation models (e.g. dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) models, Markov-switching GARCH models and multivariate stochastic volatility models), which indicates that it can describe many types of time-varying causal effects. Time-varying causal relations between West Texas Intermediate (WTI) crude oil and the US Standard and Poor's 500 (S&P 500) stock markets are examined by the proposed model. The empirical results show that their causal relations evolve with time and display complex characters. Both positive and negative causal effects of the WTI on the S&P 500 in the subperiods have been found and confirmed by the traditional VAR models. Similar results have been obtained in the causal effects of S&P 500 on WTI. In addition, the proposed model outperforms the traditional VAR model. Copyright Â© 2016 Elsevier Ltd. All rights reserved.
Ou, Lu; Chow, Sy-Miin; Ji, Linying; Molenaar, Peter C M
2017-01-01
The autoregressive latent trajectory (ALT) model synthesizes the autoregressive model and the latent growth curve model. The ALT model is flexible enough to produce a variety of discrepant model-implied change trajectories. While some researchers consider this a virtue, others have cautioned that this may confound interpretations of the model's parameters. In this article, we show that some-but not all-of these interpretational difficulties may be clarified mathematically and tested explicitly via likelihood ratio tests (LRTs) imposed on the initial conditions of the model. We show analytically the nested relations among three variants of the ALT model and the constraints needed to establish equivalences. A Monte Carlo simulation study indicated that LRTs, particularly when used in combination with information criterion measures, can allow researchers to test targeted hypotheses about the functional forms of the change process under study. We further demonstrate when and how such tests may justifiably be used to facilitate our understanding of the underlying process of change using a subsample (N = 3,995) of longitudinal family income data from the National Longitudinal Survey of Youth.
Directory of Open Access Journals (Sweden)
Mei-Yu LEE
2014-11-01
Full Text Available This paper investigates the effect of the nonzero autocorrelation coefficients on the sampling distributions of the Durbin-Watson test estimator in three time-series models that have different variance-covariance matrix assumption, separately. We show that the expected values and variances of the Durbin-Watson test estimator are slightly different, but the skewed and kurtosis coefficients are considerably different among three models. The shapes of four coefficients are similar between the Durbin-Watson model and our benchmark model, but are not the same with the autoregressive model cut by one-lagged period. Second, the large sample case shows that the three models have the same expected values, however, the autoregressive model cut by one-lagged period explores different shapes of variance, skewed and kurtosis coefficients from the other two models. This implies that the large samples lead to the same expected values, 2(1 – ρ0, whatever the variance-covariance matrix of the errors is assumed. Finally, comparing with the two sample cases, the shape of each coefficient is almost the same, moreover, the autocorrelation coefficients are negatively related with expected values, are inverted-U related with variances, are cubic related with skewed coefficients, and are U related with kurtosis coefficients.
Liu, Siwei; Molenaar, Peter C M
2014-12-01
This article introduces iVAR, an R program for imputing missing data in multivariate time series on the basis of vector autoregressive (VAR) models. We conducted a simulation study to compare iVAR with three methods for handling missing data: listwise deletion, imputation with sample means and variances, and multiple imputation ignoring time dependency. The results showed that iVAR produces better estimates for the cross-lagged coefficients than do the other three methods. We demonstrate the use of iVAR with an empirical example of time series electrodermal activity data and discuss the advantages and limitations of the program.
Directory of Open Access Journals (Sweden)
Syarif Hidayatullah
2017-04-01
Full Text Available Penelitian ini membahas analisis risiko data runtun waktu dengan model Value at Risk- Asymmetric Power Autoregressive Conditional Heteroscedasticity (VaR-APARCHdalam pasar modal syariah. Metode yang digunakan dalam penelitian ini adalah penerapan kasus.Data yang digunakan adalah harga penutupan harian saham dalam Jakarta Islamic Index (JIIperiode 4 Maret 2013 sampai 8 April 2015.Model APARCH yang dipilih berdasarkan nilai Schwarz Criterion (SC.Langkah-langkah dalam penelitian ini adalah menguji kestasioneran data, mengidentifikasi model ARIMA,mengestimasi parameter model ARIMA, menguji diagnostik model ARIMA, mendeteksi ada tidaknya unsur ARCH atau unsur heteroskedastisitas, uji asimetris data saham, mengestimasi model APARCH, menguji diagnostik model APARCH, dan menghitung risiko dengan VaR-APARCH.Model terbaik yang dipilih adalah ARIMA ((3,0,0 dan APARCH (1,1. Model ini valid untuk menganalisis besar risiko investasi dalam jangka waktu 10 hari ke depan.
Directory of Open Access Journals (Sweden)
C. Serio
1997-06-01
Full Text Available The time dynamics of geoelectrical precursory time series has been investigated and a method to discriminate chaotic behaviour in geoelectrical precursory time series is proposed. It allows us to detect low-dimensional chaos when the only information about the time series comes from the time series themselves. The short-term predictability of these time series is evaluated using two possible forecasting approaches: global autoregressive approximation and local autoregressive approximation. The first views the data as a realization of a linear stochastic process, whereas the second considers the data points as a realization of a deterministic process, supposedly non-linear. The comparison of the predictive skill of the two techniques is a test to discriminate between low-dimensional chaos and random dynamics. The analyzed time series are geoelectrical measurements recorded by an automatic station located in Tito (Southern Italy in one of the most seismic areas of the Mediterranean region. Our findings are that the global (linear approach is superior to the local one and the physical system governing the phenomena of electrical nature is characterized by a large number of degrees of freedom. Power spectra of the filtered time series follow a P(f = F-a scaling law: they exhibit the typical behaviour of a broad class of fractal stochastic processes and they are a signature of the self-organized systems.
Business cycles and fertility dynamics in the United States: a vector autoregressive model.
Mocan, N H
1990-01-01
"Using vector-autoregressions...this paper shows that fertility moves countercyclically over the business cycle....[It] shows that the United States fertility is not governed by a deterministic trend as was assumed by previous studies. Rather, fertility evolves around a stochastic trend. It is shown that a bivariate analysis between fertility and unemployment yields a procyclical picture of fertility. However, when one considers the effects on fertility of early marriages and the divorce behavior as well as economic activity, fertility moves countercyclically." excerpt
African Journals Online (AJOL)
2017-09-10
Sep 10, 2017 ... The NAR identification process is done in two steps namely model structure selection and parameter .... with MATLAB 2014a as the development platform. ..... on Modeling, Simulation and Applied Optimization, 2011, pp. 1-5.
Optimal transformations for categorical autoregressive time series
Buuren, S. van
1996-01-01
This paper describes a method for finding optimal transformations for analyzing time series by autoregressive models. 'Optimal' implies that the agreement between the autoregressive model and the transformed data is maximal. Such transformations help 1) to increase the model fit, and 2) to analyze
Zhao, Yu Xi; Xie, Ping; Sang, Yan Fang; Wu, Zi Yi
2018-04-01
Hydrological process evaluation is temporal dependent. Hydrological time series including dependence components do not meet the data consistency assumption for hydrological computation. Both of those factors cause great difficulty for water researches. Given the existence of hydrological dependence variability, we proposed a correlationcoefficient-based method for significance evaluation of hydrological dependence based on auto-regression model. By calculating the correlation coefficient between the original series and its dependence component and selecting reasonable thresholds of correlation coefficient, this method divided significance degree of dependence into no variability, weak variability, mid variability, strong variability, and drastic variability. By deducing the relationship between correlation coefficient and auto-correlation coefficient in each order of series, we found that the correlation coefficient was mainly determined by the magnitude of auto-correlation coefficient from the 1 order to p order, which clarified the theoretical basis of this method. With the first-order and second-order auto-regression models as examples, the reasonability of the deduced formula was verified through Monte-Carlo experiments to classify the relationship between correlation coefficient and auto-correlation coefficient. This method was used to analyze three observed hydrological time series. The results indicated the coexistence of stochastic and dependence characteristics in hydrological process.
Gil, Enrique A; Aubert, Xavier L; Møst, Els I S; Beersma, Domien G M
Phase estimation of the human circadian rhythm is a topic that has been explored using various modeling approaches. The current models range from physiological to mathematical, all attempting to estimate the circadian phase from different physiological or behavioral signals. Here, we have focused on
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
2008-01-01
Better modelling and forecasting of very short-term power fluctuations at large offshore wind farms may significantly enhance control and management strategies of their power output. The paper introduces a new methodology for modelling and forecasting such very short-term fluctuations. The proposed...... consists in 1-step ahead forecasting exercise on time-series of wind generation with a time resolution of 10 minute. The quality of the introduced forecasting methodology and its interest for better understanding power fluctuations are finally discussed....... methodology is based on a Markov-switching autoregressive model with time-varying coefficients. An advantage of the method is that one can easily derive full predictive densities. The quality of this methodology is demonstrated from the test case of 2 large offshore wind farms in Denmark. The exercise...
Smith, Tony E.; Lee, Ka Lok
2012-01-01
There is a common belief that the presence of residual spatial autocorrelation in ordinary least squares (OLS) regression leads to inflated significance levels in beta coefficients and, in particular, inflated levels relative to the more efficient spatial error model (SEM). However, our simulations show that this is not always the case. Hence, the purpose of this paper is to examine this question from a geometric viewpoint. The key idea is to characterize the OLS test statistic in terms of angle cosines and examine the geometric implications of this characterization. Our first result is to show that if the explanatory variables in the regression exhibit no spatial autocorrelation, then the distribution of test statistics for individual beta coefficients in OLS is independent of any spatial autocorrelation in the error term. Hence, inferences about betas exhibit all the optimality properties of the classic uncorrelated error case. However, a second more important series of results show that if spatial autocorrelation is present in both the dependent and explanatory variables, then the conventional wisdom is correct. In particular, even when an explanatory variable is statistically independent of the dependent variable, such joint spatial dependencies tend to produce "spurious correlation" that results in over-rejection of the null hypothesis. The underlying geometric nature of this problem is clarified by illustrative examples. The paper concludes with a brief discussion of some possible remedies for this problem.
Oil Price Volatility and Economic Growth in Nigeria: a Vector Auto-Regression (VAR Approach
Directory of Open Access Journals (Sweden)
Edesiri Godsday Okoro
2014-02-01
Full Text Available The study examined oil price volatility and economic growth in Nigeria linking oil price volatility, crude oil prices, oil revenue and Gross Domestic Product. Using quarterly data sourced from the Central Bank of Nigeria (CBN Statistical Bulletin and World Bank Indicators (various issues spanning 1980-2010, a non‐linear model of oil price volatility and economic growth was estimated using the VAR technique. The study revealed that oil price volatility has significantly influenced the level of economic growth in Nigeria although; the result additionally indicated a negative relationship between the oil price volatility and the level of economic growth. Furthermore, the result also showed that the Nigerian economy survived on crude oil, to such extent that the country‘s budget is tied to particular price of crude oil. This is not a good sign for a developing economy, more so that the country relies almost entirely on revenue of the oil sector as a source of foreign exchange earnings. This therefore portends some dangers for the economic survival of Nigeria. It was recommended amongst others that there should be a strong need for policy makers to focus on policy that will strengthen/stabilize the economy with specific focus on alternative sources of government revenue. Finally, there should be reduction in monetization of crude oil receipts (fiscal discipline, aggressive saving of proceeds from oil booms in future in order to withstand vicissitudes of oil price volatility in future.
DEFF Research Database (Denmark)
Silvennoinen, Annastiina; Teräsvirta, Timo
In this paper we propose a multivariate GARCH model with a time-varying conditional correlation structure. The new Double Smooth Transition Conditional Correlation GARCH model extends the Smooth Transition Conditional Correlation GARCH model of Silvennoinen and Ter¨asvirta (2005) by including...... another variable according to which the correlations change smoothly between states of constant correlations. A Lagrange multiplier test is derived to test the constancy of correlations against the DSTCC-GARCH model, and another one to test for another transition in the STCC-GARCH framework. In addition......, other specification tests, with the aim of aiding the model building procedure, are considered. Analytical expressions for the test statistics and the required derivatives are provided. The model is applied to a selection of world stock indices, and it is found that time is an important factor affecting...
Valuing structure, model uncertainty and model averaging in vector autoregressive processes
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2004-01-01
textabstractEconomic policy decisions are often informed by empirical analysis based on accurate econometric modeling. However, a decision-maker is usually only interested in good estimates of outcomes, while an analyst must also be interested in estimating the model. Accurate inference on
Directory of Open Access Journals (Sweden)
Teheni El Ghak
2017-05-01
Full Text Available In recent years, the changing economic and political environment in Tunisia led to a renewed interest on the drivers of foreign direct investment, given its potential important gains. In this study, we investigated the impact of various factors over the period 1980-2012. In doing this, three categories of determinants were considered: economic, political and sociocultural variables. Empirical findings drawn from the autoregressive distributed lag bounds testing approach show that variation in foreign direct investment inflow in the short-run and long-run is affected by the majority of variables considered, except exchange rate, urban population and gross domestic savings. As a matter of policy, it is essential that government should continue its efforts to create a macroeconomic environment which is attractive to foreign direct investment.
Directory of Open Access Journals (Sweden)
Zina Boussaada
2018-03-01
Full Text Available The solar photovoltaic (PV energy has an important place among the renewable energy sources. Therefore, several researchers have been interested by its modelling and its prediction, in order to improve the management of the electrical systems which include PV arrays. Among the existing techniques, artificial neural networks have proved their performance in the prediction of the solar radiation. However, the existing neural network models don’t satisfy the requirements of certain specific situations such as the one analyzed in this paper. The aim of this research work is to supply, with electricity, a race sailboat using exclusively renewable sources. The developed solution predicts the direct solar radiation on a horizontal surface. For that, a Nonlinear Autoregressive Exogenous (NARX neural network is used. All the specific conditions of the sailboat operation are taken into account. The results show that the best prediction performance is obtained when the training phase of the neural network is performed periodically.
International Nuclear Information System (INIS)
Marseguerra, M.; Minoggio, S.; Rossi, A.; Zio, E.
1992-01-01
The correlated noise affecting many industrial plants under stationary or cyclo-stationary conditions - nuclear reactors included -has been successfully modeled by autoregressive moving average (ARMA) due to the versatility of this technique. The relatively recent neural network methods have similar features and much effort is being devoted to exploring their usefulness in forecasting and control. Identifying a signal by means of an ARMA model gives rise to the problem of selecting its correct order. Similar difficulties must be faced when applying neural network methods and, specifically, particular care must be given to the setting up of the appropriate network topology, the data normalization procedure and the learning code. In the present paper the capability of some neural networks of learning ARMA and seasonal ARMA processes is investigated. The results of the tested cases look promising since they indicate that the neural networks learn the underlying process with relative ease so that their forecasting capability may represent a convenient fault diagnosis tool. (Author)
International Nuclear Information System (INIS)
Xu, Bin; Lin, Boqiang
2015-01-01
Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption. China's transport sector is highly energy-consuming and pollution-intensive. Between 1980 and 2012, the carbon dioxide emissions in China's transport sector increased approximately 9.7 times, with an average annual growth rate of 7.4%. Identifying the driving forces of the increase in carbon dioxide emissions in the transport sector is vital to developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the sector. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Private vehicles have more impact on emission reduction than cargo turnover due to the surge in private car population and its low energy efficiency. Urbanization also has significant effect on carbon dioxide emissions because of large-scale population movements and the transformation of the industrial structure. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the transport sector. - Highlights: • The driving forces of CO 2 emissions in China's transport sector were investigated. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to large-scale migration. • The role of private cars in reducing emissions is more important than cargo turnover
DEFF Research Database (Denmark)
Pinson, Pierre; Madsen, Henrik
optimized is based on penalized maximum-likelihood, with exponential forgetting of past observations. MSAR models are then employed for 1-step-ahead point forecasting of 10-minute resolution time-series of wind power at two large offshore wind farms. They are favourably compared against persistence and Auto......Wind power production data at temporal resolutions of a few minutes exhibits successive periods with fluctuations of various dynamic nature and magnitude, which cannot be explained (so far) by the evolution of some explanatory variable. Our proposal is to capture this regime-switching behaviour......Regressive (AR) models. It is finally shown that the main interest of MSAR models lies in their ability to generate interval/density forecasts of significantly higher skill....
Behavioural Pattern of Causality Parameter of Autoregressive ...
African Journals Online (AJOL)
In this paper, a causal form of Autoregressive Moving Average process, ARMA (p, q) of various orders and behaviour of the causality parameter of ARMA model is investigated. It is deduced that the behaviour of causality parameter ψi depends on positive and negative values of autoregressive parameter φ and moving ...
Mixed Causal-Noncausal Autoregressions with Strictly Exogenous Regressors
Hecq, Alain; Issler, J.V.; Telg, Sean
2017-01-01
The mixed autoregressive causal-noncausal model (MAR) has been proposed to estimate economic relationships involving explosive roots in their autoregressive part, as they have stationary forward solutions. In previous work, possible exogenous variables in economic relationships are substituted into
Directory of Open Access Journals (Sweden)
Cristhian Moreno-Chaparro
2011-12-01
Full Text Available This paper proposes a monthly electricity forecast method for the National Interconnected System (SIN of Colombia. The method preprocesses the time series using a Multiresolution Analysis (MRA with Discrete Wavelet Transform (DWT; a study for the selection of the mother wavelet and her order, as well as the level decomposition was carried out. Given that original series follows a non-linear behaviour, a neural nonlinear autoregressive (NAR model was used. The prediction was obtained by adding the forecast trend with the estimated obtained by the residual series combined with further components extracted from preprocessing. A bibliographic review of studies conducted internationally and in Colombia is included, in addition to references to investigations made with wavelet transform applied to electric energy prediction and studies reporting the use of NAR in prediction.
Forecasting Hourly Water Demands With Seasonal Autoregressive Models for Real-Time Application
Chen, Jinduan; Boccelli, Dominic L.
2018-02-01
Consumer water demands are not typically measured at temporal or spatial scales adequate to support real-time decision making, and recent approaches for estimating unobserved demands using observed hydraulic measurements are generally not capable of forecasting demands and uncertainty information. While time series modeling has shown promise for representing total system demands, these models have generally not been evaluated at spatial scales appropriate for representative real-time modeling. This study investigates the use of a double-seasonal time series model to capture daily and weekly autocorrelations to both total system demands and regional aggregated demands at a scale that would capture demand variability across a distribution system. Emphasis was placed on the ability to forecast demands and quantify uncertainties with results compared to traditional time series pattern-based demand models as well as nonseasonal and single-seasonal time series models. Additional research included the implementation of an adaptive-parameter estimation scheme to update the time series model when unobserved changes occurred in the system. For two case studies, results showed that (1) for the smaller-scale aggregated water demands, the log-transformed time series model resulted in improved forecasts, (2) the double-seasonal model outperformed other models in terms of forecasting errors, and (3) the adaptive adjustment of parameters during forecasting improved the accuracy of the generated prediction intervals. These results illustrate the capabilities of time series modeling to forecast both water demands and uncertainty estimates at spatial scales commensurate for real-time modeling applications and provide a foundation for developing a real-time integrated demand-hydraulic model.
Jones, A. L.; Smart, P. L.
2005-08-01
Autoregressive modelling is used to investigate the internal structure of long-term (1935-1999) records of nitrate concentration for five karst springs in the Mendip Hills. There is a significant short term (1-2 months) positive autocorrelation at three of the five springs due to the availability of sufficient nitrate within the soil store to maintain concentrations in winter recharge for several months. The absence of short term (1-2 months) positive autocorrelation in the other two springs is due to the marked contrast in land use between the limestone and swallet parts of the catchment, rapid concentrated recharge from the latter causing short term switching in the dominant water source at the spring and thus fluctuating nitrate concentrations. Significant negative autocorrelation is evident at lags varying from 4 to 7 months through to 14-22 months for individual springs, with positive autocorrelation at 19-20 months at one site. This variable timing is explained by moderation of the exhaustion effect in the soil by groundwater storage, which gives longer residence times in large catchments and those with a dominance of diffuse flow. The lags derived from autoregressive modelling may therefore provide an indication of average groundwater residence times. Significant differences in the structure of the autocorrelation function for successive 10-year periods are evident at Cheddar Spring, and are explained by the effect the ploughing up of grasslands during the Second World War and increased fertiliser usage on available nitrogen in the soil store. This effect is moderated by the influence of summer temperatures on rates of mineralization, and of both summer and winter rainfall on the timing and magnitude of nitrate leaching. The pattern of nitrate leaching also appears to have been perturbed by the 1976 drought.
International Nuclear Information System (INIS)
Geraldo, Issa Cherif; Bose, Tanmoy; Pekpe, Komi Midzodzi; Cassar, Jean-Philippe; Mohanty, A.R.; Paumel, Kévin
2014-01-01
Highlights: • The work deals with sodium boiling detection in a liquid metal fast breeder reactor. • The authors choose to use acoustic data instead of thermal data. • The method is designed to not to be disturbed by the environment noises. • A real time boiling detection methods are proposed in the paper. - Abstract: This paper deals with acoustic monitoring of sodium boiling in a liquid metal fast breeder reactor (LMFBR) based on auto regressive (AR) models which have low computational complexities. Some authors have used AR models for sodium boiling or sodium–water reaction detection. These works are based on the characterization of the difference between fault free condition and current functioning of the system. However, even in absence of faults, it is possible to observe a change in the AR models due to the change of operating mode of the LMFBR. This sets up the delicate problem of how to distinguish a change in operating mode in absence of faults and a change due to presence of faults. In this paper we propose a new approach for boiling detection based on the estimation of AR models on sliding windows. Afterwards, classification of the models into boiling or non-boiling models is made by comparing their coefficients by two statistical methods, multiple linear regression (LR) and support vectors machines (SVM). The proposed approach takes into account operating mode information in order to avoid false alarms. Experimental data include non-boiling background noise data collected from Phenix power plant (France) and provided by the CEA (Commissariat à l’Energie Atomique et aux énergies alternatives, France) and boiling condition data generated in laboratory. High boiling detection rates as well as low false alarms rates obtained on these experimental data show that the proposed method is efficient for boiling detection. Most importantly, it shows that the boiling phenomenon introduces a disturbance into the AR models that can be clearly detected
Lana, X.; Burgueño, A.; Serra, C.; Martínez, M. D.
2017-01-01
Dry spell lengths, DSL, defined as the number of consecutive days with daily rain amounts below a given threshold, may provide relevant information about drought regimes. Taking advantage of a daily pluviometric database covering a great extension of Europe, a detailed analysis of the multifractality of the dry spell regimes is achieved. At the same time, an autoregressive process is applied with the aim of predicting DSL. A set of parameters, namely Hurst exponent, H, estimated from multifractal spectrum, f( α), critical Hölder exponent, α 0, for which f( α) reaches its maximum value, spectral width, W, and spectral asymmetry, B, permits a first clustering of European rain gauges in terms of the complexity of their DSL series. This set of parameters also allows distinguishing between time series describing fine- or smooth-structure of the DSL regime by using the complexity index, CI. Results of previous monofractal analyses also permits establishing comparisons between smooth-structures, relatively low correlation dimensions, notable predictive instability and anti-persistence of DSL for European areas, sometimes submitted to long droughts. Relationships are also found between the CI and the mean absolute deviation, MAD, and the optimum autoregressive order, OAO, of an ARIMA( p, d,0) autoregressive process applied to the DSL series. The detailed analysis of the discrepancies between empiric and predicted DSL underlines the uncertainty over predictability of long DSL, particularly for the Mediterranean region.
Directory of Open Access Journals (Sweden)
Chieh-Fan Chen
2011-01-01
Full Text Available This study analyzed meteorological, clinical and economic factors in terms of their effects on monthly ED revenue and visitor volume. Monthly data from January 1, 2005 to September 30, 2009 were analyzed. Spearman correlation and cross-correlation analyses were performed to identify the correlation between each independent variable, ED revenue, and visitor volume. Autoregressive integrated moving average (ARIMA model was used to quantify the relationship between each independent variable, ED revenue, and visitor volume. The accuracies were evaluated by comparing model forecasts to actual values with mean absolute percentage of error. Sensitivity of prediction errors to model training time was also evaluated. The ARIMA models indicated that mean maximum temperature, relative humidity, rainfall, non-trauma, and trauma visits may correlate positively with ED revenue, but mean minimum temperature may correlate negatively with ED revenue. Moreover, mean minimum temperature and stock market index fluctuation may correlate positively with trauma visitor volume. Mean maximum temperature, relative humidity and stock market index fluctuation may correlate positively with non-trauma visitor volume. Mean maximum temperature and relative humidity may correlate positively with pediatric visitor volume, but mean minimum temperature may correlate negatively with pediatric visitor volume. The model also performed well in forecasting revenue and visitor volume.
Altomare, Albino; Cesario, Eugenio; Mastroianni, Carlo
2016-10-01
The opportunity of using Cloud resources on a pay-as-you-go basis and the availability of powerful data centers and high bandwidth connections are speeding up the success and popularity of Cloud systems, which is making on-demand computing a common practice for enterprises and scientific communities. The reasons for this success include natural business distribution, the need for high availability and disaster tolerance, the sheer size of their computational infrastructure, and/or the desire to provide uniform access times to the infrastructure from widely distributed client sites. Nevertheless, the expansion of large data centers is resulting in a huge rise of electrical power consumed by hardware facilities and cooling systems. The geographical distribution of data centers is becoming an opportunity: the variability of electricity prices, environmental conditions and client requests, both from site to site and with time, makes it possible to intelligently and dynamically (re)distribute the computational workload and achieve as diverse business goals as: the reduction of costs, energy consumption and carbon emissions, the satisfaction of performance constraints, the adherence to Service Level Agreement established with users, etc. This paper proposes an approach that helps to achieve the business goals established by the data center administrators. The workload distribution is driven by a fitness function, evaluated for each data center, which weighs some key parameters related to business objectives, among which, the price of electricity, the carbon emission rate, the balance of load among the data centers etc. For example, the energy costs can be reduced by using a "follow the moon" approach, e.g. by migrating the workload to data centers where the price of electricity is lower at that time. Our approach uses data about historical usage of the data centers and data about environmental conditions to predict, with the help of regressive models, the values of the
Directory of Open Access Journals (Sweden)
Alev Dilek Aydin
2015-01-01
Full Text Available The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.
Aydin, Alev Dilek; Caliskan Cavdar, Seyma
2015-01-01
The ANN method has been applied by means of multilayered feedforward neural networks (MLFNs) by using different macroeconomic variables such as the exchange rate of USD/TRY, gold prices, and the Borsa Istanbul (BIST) 100 index based on monthly data over the period of January 2000 and September 2014 for Turkey. Vector autoregressive (VAR) method has also been applied with the same variables for the same period of time. In this study, different from other studies conducted up to the present, ENCOG machine learning framework has been used along with JAVA programming language in order to constitute the ANN. The training of network has been done by resilient propagation method. The ex post and ex ante estimates obtained by the ANN method have been compared with the results obtained by the econometric forecasting method of VAR. Strikingly, our findings based on the ANN method reveal that there is a possibility of financial distress or a financial crisis in Turkey starting from October 2017. The results which were obtained with the method of VAR also support the results of ANN method. Additionally, our results indicate that the ANN approach has more superior prediction performance than the VAR method.
Bivariate autoregressive state-space modeling of psychophysiological time series data.
Smith, Daniel M; Abtahi, Mohammadreza; Amiri, Amir Mohammad; Mankodiya, Kunal
2016-08-01
Heart rate (HR) and electrodermal activity (EDA) are often used as physiological measures of psychological arousal in various neuropsychology experiments. In this exploratory study, we analyze HR and EDA data collected from four participants, each with a history of suicidal tendencies, during a cognitive task known as the Paced Auditory Serial Addition Test (PASAT). A central aim of this investigation is to guide future research by assessing heterogeneity in the population of individuals with suicidal tendencies. Using a state-space modeling approach to time series analysis, we evaluate the effect of an exogenous input, i.e., the stimulus presentation rate which was increased systematically during the experimental task. Participants differed in several parameters characterizing the way in which psychological arousal was experienced during the task. Increasing the stimulus presentation rate was associated with an increase in EDA in participants 2 and 4. The effect on HR was positive for participant 2 and negative for participants 3 and 4. We discuss future directions in light of the heterogeneity in the population indicated by these findings.
Texture classification using autoregressive filtering
Lawton, W. M.; Lee, M.
1984-01-01
A general theory of image texture models is proposed and its applicability to the problem of scene segmentation using texture classification is discussed. An algorithm, based on half-plane autoregressive filtering, which optimally utilizes second order statistics to discriminate between texture classes represented by arbitrary wide sense stationary random fields is described. Empirical results of applying this algorithm to natural and sysnthesized scenes are presented and future research is outlined.
Wang, Yiyi; Kockelman, Kara M
2013-11-01
This work examines the relationship between 3-year pedestrian crash counts across Census tracts in Austin, Texas, and various land use, network, and demographic attributes, such as land use balance, residents' access to commercial land uses, sidewalk density, lane-mile densities (by roadway class), and population and employment densities (by type). The model specification allows for region-specific heterogeneity, correlation across response types, and spatial autocorrelation via a Poisson-based multivariate conditional auto-regressive (CAR) framework and is estimated using Bayesian Markov chain Monte Carlo methods. Least-squares regression estimates of walk-miles traveled per zone serve as the exposure measure. Here, the Poisson-lognormal multivariate CAR model outperforms an aspatial Poisson-lognormal multivariate model and a spatial model (without cross-severity correlation), both in terms of fit and inference. Positive spatial autocorrelation emerges across neighborhoods, as expected (due to latent heterogeneity or missing variables that trend in space, resulting in spatial clustering of crash counts). In comparison, the positive aspatial, bivariate cross correlation of severe (fatal or incapacitating) and non-severe crash rates reflects latent covariates that have impacts across severity levels but are more local in nature (such as lighting conditions and local sight obstructions), along with spatially lagged cross correlation. Results also suggest greater mixing of residences and commercial land uses is associated with higher pedestrian crash risk across different severity levels, ceteris paribus, presumably since such access produces more potential conflicts between pedestrian and vehicle movements. Interestingly, network densities show variable effects, and sidewalk provision is associated with lower severe-crash rates. Copyright © 2013 Elsevier Ltd. All rights reserved.
on the performance of Autoregressive Moving Average Polynomial
African Journals Online (AJOL)
Timothy Ademakinwa
Distributed Lag (PDL) model, Autoregressive Polynomial Distributed Lag ... Moving Average Polynomial Distributed Lag (ARMAPDL) model. ..... Global Journal of Mathematics and Statistics. Vol. 1. ... Business and Economic Research Center.
A Realistic Process Example for MIMO MPC based on Autoregressive Models
DEFF Research Database (Denmark)
Huusom, Jakob Kjøbsted; Jørgensen, John Bagterp
2014-01-01
for advanced control design develo pment which may be used by non experts in control theory. This paper presents and illustra tes the use of a simple methodology to design an offset-free MPC based on ARX models. Hence a mecha nistic process model is not required. The forced circulation evaporator by Newell...... and Lee is used to illustrate the offset-free MPC based on ARX models for a nonlinear multivariate process ....
DEFF Research Database (Denmark)
Amado, Cristina; Teräsvirta, Timo
-run and the short-run dynamic behaviour of the volatilities. The structure of the conditional correlation matrix is assumed to be either time independent or to vary over time. We apply our model to pairs of seven daily stock returns belonging to the S&P 500 composite index and traded at the New York Stock Exchange......In this paper we investigate the effects of careful modelling the long-run dynamics of the volatilities of stock market returns on the conditional correlation structure. To this end we allow the individual unconditional variances in Conditional Correlation GARCH models to change smoothly over time...... by incorporating a nonstationary component in the variance equations. The modelling technique to determine the parametric structure of this time-varying component is based on a sequence of specification Lagrange multiplier-type tests derived in Amado and Teräsvirta (2011). The variance equations combine the long...
Non-contact video-based vital sign monitoring using ambient light and auto-regressive models
International Nuclear Information System (INIS)
Tarassenko, L; Villarroel, M; Guazzi, A; Jorge, J; Clifton, D A; Pugh, C
2014-01-01
Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient’s face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter. (paper)
Sood, Mehak; Besson, Pierre; Muthalib, Makii; Jindal, Utkarsh; Perrey, Stephane; Dutta, Anirban; Hayashibe, Mitsuhiro
2016-12-01
Transcranial direct current stimulation (tDCS) has been shown to perturb both cortical neural activity and hemodynamics during (online) and after the stimulation, however mechanisms of these tDCS-induced online and after-effects are not known. Here, online resting-state spontaneous brain activation may be relevant to monitor tDCS neuromodulatory effects that can be measured using electroencephalography (EEG) in conjunction with near-infrared spectroscopy (NIRS). We present a Kalman Filter based online parameter estimation of an autoregressive (ARX) model to track the transient coupling relation between the changes in EEG power spectrum and NIRS signals during anodal tDCS (2mA, 10min) using a 4×1 ring high-definition montage. Our online ARX parameter estimation technique using the cross-correlation between log (base-10) transformed EEG band-power (0.5-11.25Hz) and NIRS oxy-hemoglobin signal in the low frequency (≤0.1Hz) range was shown in 5 healthy subjects to be sensitive to detect transient EEG-NIRS coupling changes in resting-state spontaneous brain activation during anodal tDCS. Conventional sliding window cross-correlation calculations suffer a fundamental problem in computing the phase relationship as the signal in the window is considered time-invariant and the choice of the window length and step size are subjective. Here, Kalman Filter based method allowed online ARX parameter estimation using time-varying signals that could capture transients in the coupling relationship between EEG and NIRS signals. Our new online ARX model based tracking method allows continuous assessment of the transient coupling between the electrophysiological (EEG) and the hemodynamic (NIRS) signals representing resting-state spontaneous brain activation during anodal tDCS. Published by Elsevier B.V.
A Vector Autoregressive Model for Electricity Prices Subject to Long Memory and Regime Switching
DEFF Research Database (Denmark)
Haldrup, Niels; Nielsen, Frank; Nielsen, Morten Ørregaard
2007-01-01
A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the regime states as well as the possibility of fractional cointegra- tion. The model is relevant in describing the price dynamics of electricity prices where the transmission of power is subject...... to occasional congestion periods. For a system of bilat- eral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between essen- tially a univariate price process under non-congestion and a bivariate price...
DEFF Research Database (Denmark)
Teräsvirta, Timo; Yang, Yukai
is illustrated by two applications. In the first one, the dynamic relationship between the US gasoline price and consumption is studied and possible asymmetries in it considered. The second application consists of modelling two well known Icelandic riverflow series, previously considered by many hydrologists...
A vector autoregressive model for electricity prices subject to long memory and regime switching
International Nuclear Information System (INIS)
Haldrup, Niels; Nielsen, Frank S.; Nielsen, Morten Oerregaard
2010-01-01
A regime dependent VAR model is suggested that allows long memory (fractional integration) in each of the observed regime states as well as the possibility of fractional cointegration. The model is motivated by the dynamics of electricity prices where the transmission of power is subject to occasional congestion periods. For a system of bilateral prices non-congestion means that electricity prices are identical whereas congestion makes prices depart. Hence, the joint price dynamics implies switching between a univariate price process under non-congestion and a bivariate price process under congestion. At the same time, it is an empirical regularity that electricity prices tend to show a high degree of long memory, and thus that prices may be fractionally cointegrated. Analysis of Nord Pool data shows that even though the prices are identical under non-congestion, the prices are not, in general, fractionally cointegrated in the congestion state. Hence, in most cases price convergence is a property following from regime switching rather than a conventional error correction mechanism. Finally, the suggested model is shown to deliver forecasts that are more precise compared to competing models. (author)
The Employment of spatial autoregressive models in predicting demand for natural gas
International Nuclear Information System (INIS)
Castro, Jorge Henrique de; Silva, Alexandre Pinto Alves da
2010-01-01
Develop the natural gas network is critical success factor for the distribution company. It is a decision that employs the demand given location 'x' and a future time 't' so that the net allows the best conditions for the return of the capital. In this segment, typical network industry, the spatial infra-structure vision associated to the market allows better evaluation of the business because to mitigate costs and risks. In fact, economic models little developed in order to assess the question of the location, due to its little employment by economists. The objective of this article is to analyze the application of spatial perspective in natural gas demand forecasting and to identify the models that can be employed observing issues of dependency and spatial heterogeneity; as well as the capacity of mapping of variables associated with the problem. (author)
Czech Academy of Sciences Publication Activity Database
Pavelková, Lenka; Jirsa, Ladislav
2017-01-01
Roč. 31, č. 8 (2017), s. 1184-1192 ISSN 0890-6327 R&D Projects: GA MŠk 7D12004 Institutional support: RVO:67985556 Keywords : approximate parameter estimation * ARX model * Bayesian estimation * bounded noise * Kullback-Leibler divergence * parallelotope Subject RIV: BC - Control Systems Theory OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 1.708, year: 2016 http://library.utia.cas.cz/separaty/2017/AS/pavelkova-0472081.pdf
Assessing CO2 emissions in China’s iron and steel industry: A dynamic vector autoregression model
International Nuclear Information System (INIS)
Xu, Bin; Lin, Boqiang
2016-01-01
Highlights: • We explore the driving forces of the iron and steel industry’s CO 2 emissions in China. • Energy efficiency plays a dominant role in reducing carbon dioxide emissions. • Urbanization has significant effect on CO 2 emissions due to mass real estate construction. • The role of economic growth in reducing emissions is more important than industrialization. - Abstract: Energy saving and carbon dioxide emission reduction in China is attracting increasing attention worldwide. At present, China is in the phase of rapid urbanization and industrialization, which is characterized by rapid growth of energy consumption and carbon dioxide (CO 2 ) emissions. China’s steel industry is highly energy-consuming and pollution-intensive. Between 1980 and 2013, the carbon dioxide emissions in China’s steel industry increased approximately 11 times, with an average annual growth rate of 8%. Identifying the drivers of carbon dioxide emissions in the iron and steel industry is vital for developing effective environmental policies. This study uses Vector Autoregressive model to analyze the influencing factors of the changes in carbon dioxide emissions in the industry. The results show that energy efficiency plays a dominant role in reducing carbon dioxide emissions. Urbanization also has significant effect on CO 2 emissions because of mass urban infrastructure and real estate construction. Economic growth has more impact on emission reduction than industrialization due to the massive fixed asset investment and industrial energy optimization. These findings are important for the relevant authorities in China in developing appropriate energy policy and planning for the iron and steel industry.
Suharsono, Agus; Aziza, Auliya; Pramesti, Wara
2017-12-01
Capital markets can be an indicator of the development of a country's economy. The presence of capital markets also encourages investors to trade; therefore investors need information and knowledge of which shares are better. One way of making decisions for short-term investments is the need for modeling to forecast stock prices in the period to come. Issue of stock market-stock integration ASEAN is very important. The problem is that ASEAN does not have much time to implement one market in the economy, so it would be very interesting if there is evidence whether the capital market in the ASEAN region, especially the countries of Indonesia, Malaysia, Philippines, Singapore and Thailand deserve to be integrated or still segmented. Furthermore, it should also be known and proven What kind of integration is happening: what A capital market affects only the market Other capital, or a capital market only Influenced by other capital markets, or a Capital market as well as affecting as well Influenced by other capital markets in one ASEAN region. In this study, it will compare forecasting of Indonesian share price (IHSG) with neighboring countries (ASEAN) including developed and developing countries such as Malaysia (KLSE), Singapore (SGE), Thailand (SETI), Philippines (PSE) to find out which stock country the most superior and influential. These countries are the founders of ASEAN and share price index owners who have close relations with Indonesia in terms of trade, especially exports and imports. Stock price modeling in this research is using multivariate time series analysis that is VAR (Vector Autoregressive) and VECM (Vector Error Correction Modeling). VAR and VECM models not only predict more than one variable but also can see the interrelations between variables with each other. If the assumption of white noise is not met in the VAR modeling, then the cause can be assumed that there is an outlier. With this modeling will be able to know the pattern of relationship
Dean, Roger T; Dunsmuir, William T M
2016-06-01
Many articles on perception, performance, psychophysiology, and neuroscience seek to relate pairs of time series through assessments of their cross-correlations. Most such series are individually autocorrelated: they do not comprise independent values. Given this situation, an unfounded reliance is often placed on cross-correlation as an indicator of relationships (e.g., referent vs. response, leading vs. following). Such cross-correlations can indicate spurious relationships, because of autocorrelation. Given these dangers, we here simulated how and why such spurious conclusions can arise, to provide an approach to resolving them. We show that when multiple pairs of series are aggregated in several different ways for a cross-correlation analysis, problems remain. Finally, even a genuine cross-correlation function does not answer key motivating questions, such as whether there are likely causal relationships between the series. Thus, we illustrate how to obtain a transfer function describing such relationships, informed by any genuine cross-correlations. We illustrate the confounds and the meaningful transfer functions by two concrete examples, one each in perception and performance, together with key elements of the R software code needed. The approach involves autocorrelation functions, the establishment of stationarity, prewhitening, the determination of cross-correlation functions, the assessment of Granger causality, and autoregressive model development. Autocorrelation also limits the interpretability of other measures of possible relationships between pairs of time series, such as mutual information. We emphasize that further complexity may be required as the appropriate analysis is pursued fully, and that causal intervention experiments will likely also be needed.
Penalised Complexity Priors for Stationary Autoregressive Processes
Sø rbye, Sigrunn Holbek; Rue, Haavard
2017-01-01
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.
Penalised Complexity Priors for Stationary Autoregressive Processes
Sørbye, Sigrunn Holbek
2017-05-25
The autoregressive (AR) process of order p(AR(p)) is a central model in time series analysis. A Bayesian approach requires the user to define a prior distribution for the coefficients of the AR(p) model. Although it is easy to write down some prior, it is not at all obvious how to understand and interpret the prior distribution, to ensure that it behaves according to the users\\' prior knowledge. In this article, we approach this problem using the recently developed ideas of penalised complexity (PC) priors. These prior have important properties like robustness and invariance to reparameterisations, as well as a clear interpretation. A PC prior is computed based on specific principles, where model component complexity is penalised in terms of deviation from simple base model formulations. In the AR(1) case, we discuss two natural base model choices, corresponding to either independence in time or no change in time. The latter case is illustrated in a survival model with possible time-dependent frailty. For higher-order processes, we propose a sequential approach, where the base model for AR(p) is the corresponding AR(p-1) model expressed using the partial autocorrelations. The properties of the new prior distribution are compared with the reference prior in a simulation study.
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M.
2016-01-01
Background Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display inter-related vital sign changes during situations of physiologic stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. Purpose The purpose of this article is to illustrate development of patient-specific VAR models using vital sign time series (VSTS) data in a sample of acutely ill, monitored, step-down unit (SDU) patients, and determine their Granger causal dynamics prior to onset of an incident CRI. Approach CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40–140/minute, RR = 8–36/minute, SpO2 < 85%) and persisting for 3 minutes within a 5-minute moving window (60% of the duration of the window). A 6-hour time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity; (b) appropriate lag was determined using a lag-length selection criteria; (c) the VAR model was constructed; (d) residual autocorrelation was assessed with the Lagrange Multiplier test; (e) stability of the VAR system was checked; and (f) Granger causality was evaluated in the final stable model. Results The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%) (i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing
Directory of Open Access Journals (Sweden)
Hualin Xie
2013-12-01
Full Text Available Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated signiﬁcant positive spatial correlation (p < 0.05. The results also imply that the clustering trend of ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model.
Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai
2013-01-01
Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran’s I value is 0.1646 during the 1990 to 2005 time period and indicated significant positive spatial correlation (p ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model. PMID:24384778
Xie, Hualin; Liu, Zhifei; Wang, Peng; Liu, Guiying; Lu, Fucai
2013-12-31
Ecological land is one of the key resources and conditions for the survival of humans because it can provide ecosystem services and is particularly important to public health and safety. It is extremely valuable for effective ecological management to explore the evolution mechanisms of ecological land. Based on spatial statistical analyses, we explored the spatial disparities and primary potential drivers of ecological land change in the Poyang Lake Eco-economic Zone of China. The results demonstrated that the global Moran's I value is 0.1646 during the 1990 to 2005 time period and indicated signiﬁcant positive spatial correlation (p ecological land changes weakened in the study area. Some potential driving forces were identified by applying the spatial autoregressive model in this study. The results demonstrated that the higher economic development level and industrialization rate were the main drivers for the faster change of ecological land in the study area. This study also tested the superiority of the spatial autoregressive model to study the mechanisms of ecological land change by comparing it with the traditional linear regressive model.
Nonlinear Modeling of the PEMFC Based On NNARX Approach
Shan-Jen Cheng; Te-Jen Chang; Kuang-Hsiung Tan; Shou-Ling Kuo
2015-01-01
Polymer Electrolyte Membrane Fuel Cell (PEMFC) is such a time-vary nonlinear dynamic system. The traditional linear modeling approach is hard to estimate structure correctly of PEMFC system. From this reason, this paper presents a nonlinear modeling of the PEMFC using Neural Network Auto-regressive model with eXogenous inputs (NNARX) approach. The multilayer perception (MLP) network is applied to evaluate the structure of the NNARX model of PEMFC. The validity and accurac...
Oracle Inequalities for High Dimensional Vector Autoregressions
DEFF Research Database (Denmark)
Callot, Laurent; Kock, Anders Bredahl
This paper establishes non-asymptotic oracle inequalities for the prediction error and estimation accuracy of the LASSO in stationary vector autoregressive models. These inequalities are used to establish consistency of the LASSO even when the number of parameters is of a much larger order...
International Nuclear Information System (INIS)
Yousufzai, M.A.K; Aansari, M.R.K.; Quamar, J.; Iqbal, J.; Hussain, M.A.
2010-01-01
This communication presents the development of a comprehensive characterization of ozone layer depletion (OLD) phenomenon as a physical process in the form of mathematical models that comprise the usual regression, multiple or polynomial regression and stochastic strategy. The relevance of these models has been illuminated using predicted values of different parameters under a changing environment. The information obtained from such analysis can be employed to alter the possible factors and variables to achieve optimum performance. This kind of analysis initiates a study towards formulating the phenomenon of OLD as a physical process with special reference to the stratospheric region of Pakistan. The data presented here establishes that the Auto regressive (AR) nature of modeling OLD as a physical process is an appropriate scenario rather than using usual regression. The data reported in literature suggest quantitatively the OLD is occurring in our region. For this purpose we have modeled this phenomenon using the data recorded at the Geophysical Centre Quetta during the period 1960-1999. The predictions made by this analysis are useful for public, private and other relevant organizations. (author)
Techie Quaicoe, Michael; Twenefour, Frank B K; Baah, Emmanuel M; Nortey, Ezekiel N N
2015-01-01
This research article aimed at modeling the variations in the dollar/cedi exchange rate. It examines the applicability of a range of ARCH/GARCH specifications for modeling volatility of the series. The variants considered include the ARMA, GARCH, IGARCH, EGARCH and M-GARCH specifications. The results show that the series was non stationary which resulted from the presence of a unit root in it. The ARMA (1, 1) was found to be the most suitable model for the conditional mean. From the Box-Ljung test statistics x-squared of 1476.338 with p value 0.00217 for squared returns and 16.918 with 0.0153 p values for squared residuals, the null hypothesis of no ARCH effect was rejected at 5% significance level indicating the presence of an ARCH effect in the series. ARMA (1, 1) + GARCH (1, 1) which has all parameters significant was found to be the most suitable model for the conditional mean with conditional variance, thus showing adequacy in describing the conditional mean with variance of the return series at 5% significant level. A 24 months forecast for the mean actual exchange rates and mean returns from January, 2013 to December, 2014 made also showed that the fitted model is appropriate for the data and a depreciating trend of the cedi against the dollar for forecasted period respectively.
Alwee, Razana; Shamsuddin, Siti Mariyam Hj; Sallehuddin, Roselina
2013-01-01
Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR) and autoregressive integrated moving average (ARIMA) to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
Directory of Open Access Journals (Sweden)
Razana Alwee
2013-01-01
Full Text Available Crimes forecasting is an important area in the field of criminology. Linear models, such as regression and econometric models, are commonly applied in crime forecasting. However, in real crimes data, it is common that the data consists of both linear and nonlinear components. A single model may not be sufficient to identify all the characteristics of the data. The purpose of this study is to introduce a hybrid model that combines support vector regression (SVR and autoregressive integrated moving average (ARIMA to be applied in crime rates forecasting. SVR is very robust with small training data and high-dimensional problem. Meanwhile, ARIMA has the ability to model several types of time series. However, the accuracy of the SVR model depends on values of its parameters, while ARIMA is not robust to be applied to small data sets. Therefore, to overcome this problem, particle swarm optimization is used to estimate the parameters of the SVR and ARIMA models. The proposed hybrid model is used to forecast the property crime rates of the United State based on economic indicators. The experimental results show that the proposed hybrid model is able to produce more accurate forecasting results as compared to the individual models.
R.W. Strachan (Rodney); H.K. van Dijk (Herman)
2007-01-01
textabstractA Bayesian model averaging procedure is presented within the class of vector autoregressive (VAR) processes and applied to two empirical issues. First, stability of the "Great Ratios" in U.S. macro-economic time series is investigated, together with the presence and e¤ects of permanent
International Nuclear Information System (INIS)
Murase, Kenya; Yamazaki, Youichi; Shinohara, Masaaki
2003-01-01
The purpose of this study was to investigate the feasibility of the autoregressive moving average (ARMA) model for quantification of cerebral blood flow (CBF) with dynamic susceptibility contrast-enhanced magnetic resonance imaging (DSC-MRI) in comparison with deconvolution analysis based on singular value decomposition (DA-SVD). Using computer simulations, we generated a time-dependent concentration of the contrast agent in the volume of interest (VOI) from the arterial input function (AIF) modeled as a gamma-variate function under various CBFs, cerebral blood volumes and signal-to-noise ratios (SNRs) for three different types of residue function (exponential, triangular, and box-shaped). We also considered the effects of delay and dispersion in AIF. The ARMA model and DA-SVD were used to estimate CBF values from the simulated concentration-time curves in the VOI and AIFs, and the estimated values were compared with the assumed values. We found that the CBF value estimated by the ARMA model was more sensitive to the SNR and the delay in AIF than that obtained by DA-SVD. Although the ARMA model considerably overestimated CBF at low SNRs, it estimated the CBF more accurately than did DA-SVD at high SNRs for the exponential or triangular residue function. We believe this study will contribute to an understanding of the usefulness and limitations of the ARMA model when applied to quantification of CBF with DSC-MRI. (author)
Directory of Open Access Journals (Sweden)
Juan D Velásquez
2008-12-01
Full Text Available Una red neuronal autorregresiva es estimada para el precio mensual brasileño de corto plazo de la electricidad, la cual describe mejor la dinámica de los precios que un modelo lineal autorregresivo y que un perceptrón multicapa clásico que usan las mismas entradas y neuronas en la capa oculta. El modelo propuesto es especificado usando un procedimiento estadístico basado en el contraste del radio de verosimilitud. El modelo pasa una batería de pruebas de diagnóstico. El procedimiento de especificación propuesto permite seleccionar el número de unidades en la capa oculta y las entradas a la red neuronal, usando pruebas estadísticas que tienen en cuenta la cantidad de los datos y el ajuste del modelo a la serie de precios. La especificación del modelo final demuestra que el precio para el próximo mes es una función no lineal del precio actual, de la energía afluente actual y de la energía almacenada en el embalse equivalente en el mes actual y dos meses atrás.An autoregressive neural network model is estimated for the monthly Brazilian electricity spot price, which describes the prices dynamics better than a linear autoregressive model and a classical multilayer perceptron using the same input and neurons in the hidden layer. The proposed model is specified using a statistical procedure based on a likelihood ratio test. The model passes a battery of diagnostic tests. The proposed specification procedure allows us to select the number of units in hidden layer and the inputs to the neural network based on statistical tests, taking into account the number of data and the model fitting to the price time series. The final model specification demonstrates that the price for the next month is a nonlinear function of the current price, the current energy inflow, and the energy saved in the equivalent reservoir in the current month and two months ago.
Directory of Open Access Journals (Sweden)
Wudi Wei
Full Text Available Hepatitis is a serious public health problem with increasing cases and property damage in Heng County. It is necessary to develop a model to predict the hepatitis epidemic that could be useful for preventing this disease.The autoregressive integrated moving average (ARIMA model and the generalized regression neural network (GRNN model were used to fit the incidence data from the Heng County CDC (Center for Disease Control and Prevention from January 2005 to December 2012. Then, the ARIMA-GRNN hybrid model was developed. The incidence data from January 2013 to December 2013 were used to validate the models. Several parameters, including mean absolute error (MAE, root mean square error (RMSE, mean absolute percentage error (MAPE and mean square error (MSE, were used to compare the performance among the three models.The morbidity of hepatitis from Jan 2005 to Dec 2012 has seasonal variation and slightly rising trend. The ARIMA(0,1,2(1,1,112 model was the most appropriate one with the residual test showing a white noise sequence. The smoothing factor of the basic GRNN model and the combined model was 1.8 and 0.07, respectively. The four parameters of the hybrid model were lower than those of the two single models in the validation. The parameters values of the GRNN model were the lowest in the fitting of the three models.The hybrid ARIMA-GRNN model showed better hepatitis incidence forecasting in Heng County than the single ARIMA model and the basic GRNN model. It is a potential decision-supportive tool for controlling hepatitis in Heng County.
Jia, Song; Xu, Tian-he; Sun, Zhang-zhen; Li, Jia-jing
2017-02-01
UT1-UTC is an important part of the Earth Orientation Parameters (EOP). The high-precision predictions of UT1-UTC play a key role in practical applications of deep space exploration, spacecraft tracking and satellite navigation and positioning. In this paper, a new prediction method with combination of Gray Model (GM(1, 1)) and Autoregressive Integrated Moving Average (ARIMA) is developed. The main idea is as following. Firstly, the UT1-UTC data are preprocessed by removing the leap second and Earth's zonal harmonic tidal to get UT1R-TAI data. Periodic terms are estimated and removed by the least square to get UT2R-TAI. Then the linear terms of UT2R-TAI data are modeled by the GM(1, 1), and the residual terms are modeled by the ARIMA. Finally, the UT2R-TAI prediction can be performed based on the combined model of GM(1, 1) and ARIMA, and the UT1-UTC predictions are obtained by adding the corresponding periodic terms, leap second correction and the Earth's zonal harmonic tidal correction. The results show that the proposed model can be used to predict UT1-UTC effectively with higher middle and long-term (from 32 to 360 days) accuracy than those of LS + AR, LS + MAR and WLS + MAR.
Energy Technology Data Exchange (ETDEWEB)
Biyanto, Totok R. [Department of Engineering Physics, Institute Technology of Sepuluh Nopember Surabaya, Surabaya, Indonesia 60111 (Indonesia)
2016-06-03
Fouling in a heat exchanger in Crude Preheat Train (CPT) refinery is an unsolved problem that reduces the plant efficiency, increases fuel consumption and CO{sub 2} emission. The fouling resistance behavior is very complex. It is difficult to develop a model using first principle equation to predict the fouling resistance due to different operating conditions and different crude blends. In this paper, Artificial Neural Networks (ANN) MultiLayer Perceptron (MLP) with input structure using Nonlinear Auto-Regressive with eXogenous (NARX) is utilized to build the fouling resistance model in shell and tube heat exchanger (STHX). The input data of the model are flow rates and temperatures of the streams of the heat exchanger, physical properties of product and crude blend data. This model serves as a predicting tool to optimize operating conditions and preventive maintenance of STHX. The results show that the model can capture the complexity of fouling characteristics in heat exchanger due to thermodynamic conditions and variations in crude oil properties (blends). It was found that the Root Mean Square Error (RMSE) are suitable to capture the nonlinearity and complexity of the STHX fouling resistance during phases of training and validation.
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-01-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
Directory of Open Access Journals (Sweden)
VIAN RISKA AYUNING TYAS
2014-08-01
Full Text Available The Arbitrage Pricing Theory (APT is an alternative model to estimate the price of securities based of arbitrage concept. In APT, the returns of securities are affected by several factors. This research is aimed to estimate the expected returns of securities using APT model and Vector Autoregressive model. There are ten stocks incorporated in Kompas100 index and four macroeconomic variables, these are inflation, exchange rates, the amountof circulate money (JUB, and theinterest rateof Bank Indonesia(SBI are applied in this research. The first step in using VAR is to test the stationary of the data using colerogram and the results indicate that all data are stationary. The second step is to select the optimal lag based on the smallest value of AIC. The Granger causality test shows that the LPKR stock is affected by the inflation and the exchange rate while the nine other stocks do not show the existence of the expected causality. The results of causality test are then estimated by the VAR models in order to obtain expected returnof macroeconomic factors. The expected return of macroeconomic factors obtained is used in the APT model, then the expected return stock LPKR is calculated. It shows that the expected return of LPKR is 3,340%
International Nuclear Information System (INIS)
Liu, Yaobin
2009-01-01
The paper develops a function of energy consumption, population growth, economic growth and urbanization process, and provides fresh empirical evidences for urbanization and energy consumption for China over the period 1978-2008 through the use of ARDL testing approach and factor decomposition model. The results of the bounds test show that there is a stable long run relationship amongst total energy consumption, population, GDP (Gross domestic product) and urbanization level when total energy consumption is the dependent variable in China. The results of the causality test with ECM (error correction model) specification, the short run and long run dynamics of the interested variables are tested, indicating that there exists only a unidirectional Granger causality running from urbanization to total energy consumption both in the long run and in the short run. At present, the contribution share which urbanization drags the energy consumption is smaller than that in the past, and the intensity holds a downward trend. Therefore, together with enhancing energy efficiency, accelerating the urbanization process that can cut reliance on resource and energy dependent industries is a fundamental strategy to solve the sustainable development dilemma between energy consumption and urbanization.
Energy Technology Data Exchange (ETDEWEB)
Liu, Yaobin [Research Center of the Central China Economic Development, Nanchang University, Nanchang 330047 (China)
2009-11-15
The paper develops a function of energy consumption, population growth, economic growth and urbanization process, and provides fresh empirical evidences for urbanization and energy consumption for China over the period 1978-2008 through the use of ARDL testing approach and factor decomposition model. The results of the bounds test show that there is a stable long run relationship amongst total energy consumption, population, GDP (Gross domestic product) and urbanization level when total energy consumption is the dependent variable in China. The results of the causality test with ECM (error correction model) specification, the short run and long run dynamics of the interested variables are tested, indicating that there exists only a unidirectional Granger causality running from urbanization to total energy consumption both in the long run and in the short run. At present, the contribution share which urbanization drags the energy consumption is smaller than that in the past, and the intensity holds a downward trend. Therefore, together with enhancing energy efficiency, accelerating the urbanization process that can cut reliance on resource and energy dependent industries is a fundamental strategy to solve the sustainable development dilemma between energy consumption and urbanization. (author)
Tani, Yuji; Ogasawara, Katsuhiko
2012-01-01
This study aimed to contribute to the management of a healthcare organization by providing management information using time-series analysis of business data accumulated in the hospital information system, which has not been utilized thus far. In this study, we examined the performance of the prediction method using the auto-regressive integrated moving-average (ARIMA) model, using the business data obtained at the Radiology Department. We made the model using the data used for analysis, which was the number of radiological examinations in the past 9 years, and we predicted the number of radiological examinations in the last 1 year. Then, we compared the actual value with the forecast value. We were able to establish that the performance prediction method was simple and cost-effective by using free software. In addition, we were able to build the simple model by pre-processing the removal of trend components using the data. The difference between predicted values and actual values was 10%; however, it was more important to understand the chronological change rather than the individual time-series values. Furthermore, our method was highly versatile and adaptable compared to the general time-series data. Therefore, different healthcare organizations can use our method for the analysis and forecasting of their business data.
Earnest, Arul; Chen, Mark I; Ng, Donald; Sin, Leo Yee
2005-05-11
The main objective of this study is to apply autoregressive integrated moving average (ARIMA) models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases) and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. We found that the ARIMA (1,0,3) model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE) for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious diseases as well.
Directory of Open Access Journals (Sweden)
Egil Nygaard
2017-06-01
Full Text Available Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1 and then 3 months (T2, 12 months (T3 and 8 years (T4 after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term.
Nygaard, Egil; Johansen, Venke A; Siqveland, Johan; Hussain, Ajmal; Heir, Trond
2017-01-01
Self-efficacy is assumed to promote posttraumatic adaption, and several cross-sectional studies support this notion. However, there is a lack of prospective longitudinal studies to further illuminate the temporal relationship between self-efficacy and posttraumatic stress symptoms. Thus, an important unresolved research question is whether posttraumatic stress disorder (PTSD) symptoms affect the level of self-efficacy or vice versa or whether they mutually influence each other. The present prospective longitudinal study investigated the reciprocal relationship between general self-efficacy (GSE) and posttraumatic stress symptoms in 143 physical assault victims. We used an autoregressive cross-lagged model across four assessment waves: within 4 months after the assault (T1) and then 3 months (T2), 12 months (T3) and 8 years (T4) after the first assessment. Stress symptoms at T1 and T2 predicted subsequent self-efficacy, while self-efficacy at T1 and T2 was not related to subsequent stress symptoms. These relationships were reversed after T3; higher levels of self-efficacy at T3 predicted lower levels of posttraumatic stress symptoms at T4, while posttraumatic tress symptoms at T3 did not predict self-efficacy at T4. In conclusion, posttraumatic stress symptoms may have a deteriorating effect on self-efficacy in the early phase after physical assault, whereas self-efficacy may promote recovery from posttraumatic stress symptoms over the long term.
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Gregor M Hoerzer
2010-05-01
Full Text Available Processing and storage of sensory information is based on the interaction between different neural populations rather than the isolated activity of single neurons. In order to characterize the dynamic interaction and transient cooperation of sub-circuits within a neural network, multivariate autoregressive (MVAR models have proven to be an important analysis tool. In this study, we apply directed functional coupling based on MVAR models and describe the temporal and spatial changes of functional coupling between simultaneously recorded local field potentials (LFP in extrastriate area V4 during visual memory. Specifically, we compare the strength and directional relations of coupling based on Generalized Partial Directed Coherence (GDPC measures while two rhesus monkeys perform a visual short-term memory task. In both monkeys we find increases in theta power during the memory period that are accompanied by changes in directed coupling. These interactions are most prominent in the low frequency range encompassing the theta band (3-12~Hz and, more importantly, are asymmetric between pairs of recording sites. Furthermore, we find that the degree of interaction decreases as a function of distance between electrode positions, suggesting that these interactions are a predominantly local phenomenon. Taken together, our results show that directed coupling measures based on MVAR models are able to provide important insights into the spatial and temporal formation of local functionally coupled ensembles during visual memory in V4. Moreover, our findings suggest that visual memory is accompanied not only by a temporary increase of oscillatory activity in the theta band, but by a direction-dependent change in theta coupling, which ultimately represents a change in functional connectivity within the neural circuit.
International Nuclear Information System (INIS)
Jafri, Y.Z.; Kamal, L.
2009-01-01
A generalized theory of ARMA modeling, covering a wide range of researches. with model identification, order determination, estimation and diagnostic checking is presented. We evolved standardization of wind data to overcome non-stationarity. With our techniques on generating synthetic values of wind series using MTM, we modeled and simulated autocorrelated function (ACF). MTM is found relatively a better simulator as compared to ARMA. We used twenty year of wind data. MTM required fast computation and suitable algorithm for backward calculations to yield ACF values. We found ARMA (p, q) model suitableble for both large range (1-6 hours) and short range (1-2 hours). This indicates that forecast values can be considered for appropriate wind energy conversion system. (author)
The Prediction of Exchange Rates with the Use of Auto-Regressive Integrated Moving-Average Models
Directory of Open Access Journals (Sweden)
Daniela Spiesová
2014-10-01
Full Text Available Currency market is recently the largest world market during the existence of which there have been many theories regarding the prediction of the development of exchange rates based on macroeconomic, microeconomic, statistic and other models. The aim of this paper is to identify the adequate model for the prediction of non-stationary time series of exchange rates and then use this model to predict the trend of the development of European currencies against Euro. The uniqueness of this paper is in the fact that there are many expert studies dealing with the prediction of the currency pairs rates of the American dollar with other currency but there is only a limited number of scientific studies concerned with the long-term prediction of European currencies with the help of the integrated ARMA models even though the development of exchange rates has a crucial impact on all levels of economy and its prediction is an important indicator for individual countries, banks, companies and businessmen as well as for investors. The results of this study confirm that to predict the conditional variance and then to estimate the future values of exchange rates, it is adequate to use the ARIMA (1,1,1 model without constant, or ARIMA [(1,7,1,(1,7] model, where in the long-term, the square root of the conditional variance inclines towards stable value.
Simms, Laura; Engebretson, Mark; Clilverd, Mark; Rodger, Craig; Lessard, Marc; Gjerloev, Jesper; Reeves, Geoffrey
2018-05-01
Relativistic electron flux at geosynchronous orbit depends on enhancement and loss processes driven by ultralow frequency (ULF) Pc5, chorus, and electromagnetic ion cyclotron (EMIC) waves, seed electron flux, magnetosphere compression, the "Dst effect," and substorms, while solar wind inputs such as velocity, number density, and interplanetary magnetic field Bz drive these factors and thus correlate with flux. Distributed lag regression models show the time delay of highest influence of these factors on log10 high-energy electron flux (0.7-7.8 MeV, Los Alamos National Laboratory satellites). Multiple regression with an autoregressive term (flux persistence) allows direct comparison of the magnitude of each effect while controlling other correlated parameters. Flux enhancements due to ULF Pc5 and chorus waves are of equal importance. The direct effect of substorms on high-energy electron flux is strong, possibly due to injection of high-energy electrons by the substorms themselves. Loss due to electromagnetic ion cyclotron waves is less influential. Southward Bz shows only moderate influence when correlated processes are accounted for. Adding covariate compression effects (pressure and interplanetary magnetic field magnitude) allows wave-driven enhancements to be more clearly seen. Seed electrons (270 keV) are most influential at lower relativistic energies, showing that such a population must be available for acceleration. However, they are not accelerated directly to the highest energies. Source electrons (31.7 keV) show no direct influence when other factors are controlled. Their action appears to be indirect via the chorus waves they generate. Determination of specific effects of each parameter when studied in combination will be more helpful in furthering modeling work than studying them individually.
Directory of Open Access Journals (Sweden)
Earnest Arul
2005-05-01
Full Text Available Abstract Background The main objective of this study is to apply autoregressive integrated moving average (ARIMA models to make real-time predictions on the number of beds occupied in Tan Tock Seng Hospital, during the recent SARS outbreak. Methods This is a retrospective study design. Hospital admission and occupancy data for isolation beds was collected from Tan Tock Seng hospital for the period 14th March 2003 to 31st May 2003. The main outcome measure was daily number of isolation beds occupied by SARS patients. Among the covariates considered were daily number of people screened, daily number of people admitted (including observation, suspect and probable cases and days from the most recent significant event discovery. We utilized the following strategy for the analysis. Firstly, we split the outbreak data into two. Data from 14th March to 21st April 2003 was used for model development. We used structural ARIMA models in an attempt to model the number of beds occupied. Estimation is via the maximum likelihood method using the Kalman filter. For the ARIMA model parameters, we considered the simplest parsimonious lowest order model. Results We found that the ARIMA (1,0,3 model was able to describe and predict the number of beds occupied during the SARS outbreak well. The mean absolute percentage error (MAPE for the training set and validation set were 5.7% and 8.6% respectively, which we found was reasonable for use in the hospital setting. Furthermore, the model also provided three-day forecasts of the number of beds required. Total number of admissions and probable cases admitted on the previous day were also found to be independent prognostic factors of bed occupancy. Conclusion ARIMA models provide useful tools for administrators and clinicians in planning for real-time bed capacity during an outbreak of an infectious disease such as SARS. The model could well be used in planning for bed-capacity during outbreaks of other infectious
International Nuclear Information System (INIS)
Gloeckler, O.; Upadhyaya, B.R.
1987-01-01
Multivariate noise analysis of power reactor operating signals is useful for plant diagnostics, for isolating process and sensor anomalies, and for automated plant monitoring. In order to develop a reliable procedure, the previously established techniques for empirical modeling of fluctuation signals in power reactors have been improved. Application of the complete algorithm to operational data from the Loss-of-Fluid-Test (LOFT) Reactor showed that earlier conjectures (based on physical modeling) regarding the perturbation sources in a Pressurized Water Reactor (PWR) affecting coolant temperature and neutron power fluctuations can be systematically explained. This advanced methodology has important implication regarding plant diagnostics, and system or sensor anomaly isolation. 6 refs., 24 figs
International Nuclear Information System (INIS)
Auer, Benjamin R.
2016-01-01
Based on a dynamic model for the high/low range of electricity prices, this article analyses the effects of Germany's green energy policy on the volatility of the electricity market. Using European Energy Exchange data from 2000 to 2015, we find rather high volatility in the years 2000–2009 but also that the weekly price range has significantly declined in the period following the year 2009. This period is characterised by active regulation under the Energy Industry Law (EnWG), the EU Emissions Trading Directive (ETD) and the Renewable Energy Law (EEG). In contrast to the preceding period, price jumps are smaller and less frequent (especially for day-time hours), implying that current policy measures are effective in promoting renewable energies while simultaneously upholding electricity market stability. This is because the regulations strive towards a more and more flexible and market-oriented structure which allows better integration of renewable energies and supports an efficient alignment of renewable electricity supply with demand. - Highlights: • We estimate a CARR model for German electricity price data. • We augment the model by dummies capturing important regulations. • We find a significant decline in the price range after the year 2009. • This implies effective price stabilisation by German energy policy.
Bose, Eliezer; Hravnak, Marilyn; Sereika, Susan M
Patients undergoing continuous vital sign monitoring (heart rate [HR], respiratory rate [RR], pulse oximetry [SpO2]) in real time display interrelated vital sign changes during situations of physiological stress. Patterns in this physiological cross-talk could portend impending cardiorespiratory instability (CRI). Vector autoregressive (VAR) modeling with Granger causality tests is one of the most flexible ways to elucidate underlying causal mechanisms in time series data. The purpose of this article is to illustrate the development of patient-specific VAR models using vital sign time series data in a sample of acutely ill, monitored, step-down unit patients and determine their Granger causal dynamics prior to onset of an incident CRI. CRI was defined as vital signs beyond stipulated normality thresholds (HR = 40-140/minute, RR = 8-36/minute, SpO2 time segment prior to onset of first CRI was chosen for time series modeling in 20 patients using a six-step procedure: (a) the uniform time series for each vital sign was assessed for stationarity, (b) appropriate lag was determined using a lag-length selection criteria, (c) the VAR model was constructed, (d) residual autocorrelation was assessed with the Lagrange Multiplier test, (e) stability of the VAR system was checked, and (f) Granger causality was evaluated in the final stable model. The primary cause of incident CRI was low SpO2 (60% of cases), followed by out-of-range RR (30%) and HR (10%). Granger causality testing revealed that change in RR caused change in HR (21%; i.e., RR changed before HR changed) more often than change in HR causing change in RR (15%). Similarly, changes in RR caused changes in SpO2 (15%) more often than changes in SpO2 caused changes in RR (9%). For HR and SpO2, changes in HR causing changes in SpO2 and changes in SpO2 causing changes in HR occurred with equal frequency (18%). Within this sample of acutely ill patients who experienced a CRI event, VAR modeling indicated that RR changes
DEFF Research Database (Denmark)
Zhao, Yongning; Ye, Lin; Pinson, Pierre
2018-01-01
The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity-Controlled Vec......The ever-increasing number of wind farms has brought both challenges and opportunities in the development of wind power forecasting techniques to take advantage of interdependenciesbetweentensorhundredsofspatiallydistributedwind farms, e.g., over a region. In this paper, a Sparsity...... matrices in direct manner. However this original SC-VAR is difﬁcult to implement due to its complicated constraints and the lack of guidelines for setting its parameters. To reduce the complexity of this MINLP and to make it possible to incorporate prior expert knowledge to beneﬁt model building...
Crime Modeling using Spatial Regression Approach
Saleh Ahmar, Ansari; Adiatma; Kasim Aidid, M.
2018-01-01
Act of criminality in Indonesia increased both variety and quantity every year. As murder, rape, assault, vandalism, theft, fraud, fencing, and other cases that make people feel unsafe. Risk of society exposed to crime is the number of reported cases in the police institution. The higher of the number of reporter to the police institution then the number of crime in the region is increasing. In this research, modeling criminality in South Sulawesi, Indonesia with the dependent variable used is the society exposed to the risk of crime. Modelling done by area approach is the using Spatial Autoregressive (SAR) and Spatial Error Model (SEM) methods. The independent variable used is the population density, the number of poor population, GDP per capita, unemployment and the human development index (HDI). Based on the analysis using spatial regression can be shown that there are no dependencies spatial both lag or errors in South Sulawesi.
Directory of Open Access Journals (Sweden)
Athenia Bongani Sibindi
2014-12-01
Full Text Available The life insurance sector may contribute to economic growth by its very mechanism of savings mobilisation and thereby performing an intermediation role in the economy. This ensures that capital is provided to deficient units who are in need of capital to finance their working capital requirements and invest in technology thereby resulting in an increase in output. In this way, it could be argued that life insurance development spurs financial development. In this article we investigate the causal relationship between the life insurance sector, financial development and economic growth in South Africa for the period 1990 to 2012 by applying the ARDL bounds testing procedure. We make use of life insurance density as the proxy for life insurance development, real per capita growth domestic product as the proxy for economic growth and real broad money per capita as the proxy for financial development. We test for cointegration amongst the variables by applying the bounds test and then proceed to test for Granger causality based on the error correction model. Our results confirm that the variables are cointegrated and move in tandem to each other in the long-run. The results also indicate that the direction of causality runs from the economy to the life insurance sector in the short-run which is consistent with the “demand-following” insurance-growth hypothesis. There is also evidence of bidirectional Granger causality running from the economy to financial development and vice versa, both in the long-run and short-run. The results also reveal that life insurance complements financial development in bringing about economic growth further lending credence to the “complementarity” hypothesis
Autoregressive Moving Average Graph Filtering
Isufi, Elvin; Loukas, Andreas; Simonetto, Andrea; Leus, Geert
2016-01-01
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design phi...
Testing periodically integrated autoregressive models
Ph.H.B.F. Franses (Philip Hans); M.J. McAleer (Michael)
1997-01-01
textabstractPeriodically integrated time series require a periodic differencing filter to remove the stochastic trend. A non-periodic integrated time series needs the first-difference filter for similar reasons. When the changing seasonal fluctuations for the non-periodic integrated series can be
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João Domingos Scalon
2010-07-01
Full Text Available The dairy yield is one of the most important activities for the Brazilian economy and the use of statistical models may improve the decision making in this productive sector. The aim of this paper was to compare the performance of both the traditional linear regression model and the spatial regression model called conditional autoregressive (CAR to explain how some covariates may contribute for the dairy yield. This work used a database on dairy yield supplied by the Brazilian Institute of Geography and Statistics (IBGE and another database on geographical information of the state of Minas Gerais provided by the Integrated Program of Technological Use of Geographical Information (GEOMINAS. The results showed the superiority of the CAR model over the traditional linear regression model to explain the dairy yield. The CAR model allowed the identification of two different spatial clusters of counties related to the dairy yield in the state of Minas Gerais. The first cluster represents the region where one observes the biggest levels of dairy yield. It is formed by the counties of the Triângulo Mineiro. The second cluster is formed by the northern counties of the state that present the lesser levels of dairy yield. A produção de leite é uma das atividades mais importantes para a economia brasileira e o uso de modelos estatísticos pode auxiliar a tomada de decisão neste setor produtivo. O objetivo deste artigo foi comparar o desempenho do modelo de regressão linear tradicional e do modelo de regressão espacial, denominado de autoregressivo condicional (CAR, para explicar como algumas variáveis preditoras contribuem para a quantidade de leite produzido. Este trabalho usou uma base de dados sobre a produção de leite fornecida pelo Instituto Brasileiro de Geografia e Estatística (IBGE e outra base de dados sobre informações geográficas do estado de Minas Gerais, fornecida pelo Programa Integrado de Uso da Tecnologia de Geoprocessamento
Kepler AutoRegressive Planet Search (KARPS)
Caceres, Gabriel
2018-01-01
One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The Kepler AutoRegressive Planet Search (KARPS) project implements statistical methodology associated with autoregressive processes (in particular, ARIMA and ARFIMA) to model stellar lightcurves in order to improve exoplanet transit detection. We also develop a novel Transit Comb Filter (TCF) applied to the AR residuals which provides a periodogram analogous to the standard Box-fitting Least Squares (BLS) periodogram. We train a random forest classifier on known Kepler Objects of Interest (KOIs) using select features from different stages of this analysis, and then use ROC curves to define and calibrate the criteria to recover the KOI planet candidates with high fidelity. These statistical methods are detailed in a contributed poster (Feigelson et al., this meeting).These procedures are applied to the full DR25 dataset of NASA’s Kepler mission. Using the classification criteria, a vast majority of known KOIs are recovered and dozens of new KARPS Candidate Planets (KCPs) discovered, including ultra-short period exoplanets. The KCPs will be briefly presented and discussed.
Kepler AutoRegressive Planet Search
Caceres, Gabriel Antonio; Feigelson, Eric
2016-01-01
The Kepler AutoRegressive Planet Search (KARPS) project uses statistical methodology associated with autoregressive (AR) processes to model Kepler lightcurves in order to improve exoplanet transit detection in systems with high stellar variability. We also introduce a planet-search algorithm to detect transits in time-series residuals after application of the AR models. One of the main obstacles in detecting faint planetary transits is the intrinsic stellar variability of the host star. The variability displayed by many stars may have autoregressive properties, wherein later flux values are correlated with previous ones in some manner. Our analysis procedure consisting of three steps: pre-processing of the data to remove discontinuities, gaps and outliers; AR-type model selection and fitting; and transit signal search of the residuals using a new Transit Comb Filter (TCF) that replaces traditional box-finding algorithms. The analysis procedures of the project are applied to a portion of the publicly available Kepler light curve data for the full 4-year mission duration. Tests of the methods have been made on a subset of Kepler Objects of Interest (KOI) systems, classified both as planetary `candidates' and `false positives' by the Kepler Team, as well as a random sample of unclassified systems. We find that the ARMA-type modeling successfully reduces the stellar variability, by a factor of 10 or more in active stars and by smaller factors in more quiescent stars. A typical quiescent Kepler star has an interquartile range (IQR) of ~10 e-/sec, which may improve slightly after modeling, while those with IQR ranging from 20 to 50 e-/sec, have improvements from 20% up to 70%. High activity stars (IQR exceeding 100) markedly improve. A periodogram based on the TCF is constructed to concentrate the signal of these periodic spikes. When a periodic transit is found, the model is displayed on a standard period-folded averaged light curve. Our findings to date on real
An extension of cointegration to fractional autoregressive processes
DEFF Research Database (Denmark)
Johansen, Søren
This paper contains an overview of some recent results on the statistical analysis of cofractional processes, see Johansen and Nielsen (2010). We first give an brief summary of the analysis of cointegration in the vector autoregressive model and then show how this can be extended to fractional pr...
Stable Parameter Estimation for Autoregressive Equations with Random Coefficients
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2014-01-01
Full Text Available In recent yearsthere has been a growing interest in non-linear time series models. They are more flexible than traditional linear models and allow more adequate description of real data. Among these models a autoregressive model with random coefficients plays an important role. It is widely used in various fields of science and technology, for example, in physics, biology, economics and finance. The model parameters are the mean values of autoregressive coefficients. Their evaluation is the main task of model identification. The basic method of estimation is still the least squares method, which gives good results for Gaussian time series, but it is quite sensitive to even small disturbancesin the assumption of Gaussian observations. In this paper we propose estimates, which generalize the least squares estimate in the sense that the quadratic objective function is replaced by an arbitrary convex and even function. Reasonable choice of objective function allows you to keep the benefits of the least squares estimate and eliminate its shortcomings. In particular, you can make it so that they will be almost as effective as the least squares estimate in the Gaussian case, but almost never loose in accuracy with small deviations of the probability distribution of the observations from the Gaussian distribution.The main result is the proof of consistency and asymptotic normality of the proposed estimates in the particular case of the one-parameter model describing the stationary process with finite variance. Another important result is the finding of the asymptotic relative efficiency of the proposed estimates in relation to the least squares estimate. This allows you to compare the two estimates, depending on the probability distribution of innovation process and of autoregressive coefficients. The results can be used to identify an autoregressive process, especially with nonGaussian nature, and/or of autoregressive processes observed with gross
Lee, Cameron C.; Sheridan, Scott C.; Barnes, Brian B.; Hu, Chuanmin; Pirhalla, Douglas E.; Ransibrahmanakul, Varis; Shein, Karsten
2017-10-01
The coastal waters of the southeastern USA contain important protected habitats and natural resources that are vulnerable to climate variability and singular weather events. Water clarity, strongly affected by atmospheric events, is linked to substantial environmental impacts throughout the region. To assess this relationship over the long-term, this study uses an artificial neural network-based time series modeling technique known as non-linear autoregressive models with exogenous input (NARX models) to explore the relationship between climate and a water clarity index (KDI) in this area and to reconstruct this index over a 66-year period. Results show that synoptic-scale circulation patterns, weather types, and precipitation all play roles in impacting water clarity to varying degrees in each region of the larger domain. In particular, turbid water is associated with transitional weather and cyclonic circulation in much of the study region. Overall, NARX model performance also varies—regionally, seasonally and interannually—with wintertime estimates of KDI along the West Florida Shelf correlating to the actual KDI at r > 0.70. Periods of extreme (high) KDI in this area coincide with notable El Niño events. An upward trend in extreme KDI events from 1948 to 2013 is also present across much of the Florida Gulf coast.
Characteristics of the transmission of autoregressive sub-patterns in financial time series
Gao, Xiangyun; An, Haizhong; Fang, Wei; Huang, Xuan; Li, Huajiao; Zhong, Weiqiong
2014-09-01
There are many types of autoregressive patterns in financial time series, and they form a transmission process. Here, we define autoregressive patterns quantitatively through an econometrical regression model. We present a computational algorithm that sets the autoregressive patterns as nodes and transmissions between patterns as edges, and then converts the transmission process of autoregressive patterns in a time series into a network. We utilised daily Shanghai (securities) composite index time series to study the transmission characteristics of autoregressive patterns. We found statistically significant evidence that the financial market is not random and that there are similar characteristics between parts and whole time series. A few types of autoregressive sub-patterns and transmission patterns drive the oscillations of the financial market. A clustering effect on fluctuations appears in the transmission process, and certain non-major autoregressive sub-patterns have high media capabilities in the financial time series. Different stock indexes exhibit similar characteristics in the transmission of fluctuation information. This work not only proposes a distinctive perspective for analysing financial time series but also provides important information for investors.
Prediction of municipal solid waste generation using nonlinear autoregressive network.
Younes, Mohammad K; Nopiah, Z M; Basri, N E Ahmad; Basri, H; Abushammala, Mohammed F M; Maulud, K N A
2015-12-01
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
On the Stationarity of Multiple Autoregressive Approximants: Theory and Algorithms
1976-08-01
a I (3.4) Hannan and Terrell (1972) consider problems of a similar nature. Efficient estimates A(1),... , A(p) , and i of A(1)... ,A(p) and...34Autoregressive model fitting for control, Ann . Inst. Statist. Math., 23, 163-180. Hannan, E. J. (1970), Multiple Time Series, New York, John Wiley...Hannan, E. J. and Terrell , R. D. (1972), "Time series regression with linear constraints, " International Economic Review, 13, 189-200. Masani, P
Directory of Open Access Journals (Sweden)
Nurul Huda
2015-04-01
Full Text Available Objective - Islamic banks are banks which its activities, both fund raising and funds distribution are on the basis of Islamic principles, namely buying and selling and profit sharing. Islamic banking is aimed at supporting the implementation of national development in order to improve justice, togetherness, and equitable distribution of welfare. In pursuit of supporting the implementation of national development, Islamic banking often faced stability problems of financing instruments being operated. In this case, it is measured by the gap between the actual rate of return and the expected rate of return. The individual actual RoR of this instrument will generate an expected rate of return. This raises the gap or difference between the actual rate of return and the expected rate of return of individual instruments, which in this case is called the abnormal rate of return. The stability of abnormal rate of return of individual instruments is certainly influenced by the stability of the expected rate of return. Expected rate of return has a volatility or fluctuation levels for each financing instrument. It is also a key element or material basis for the establishment of a variance of individual instruments. Variance in this case indicates the level of uncertainty of the rate of return. Individual variance is the origin of the instrument base for variance in the portfolio finance that further a portfolio analysis. So, this paper is going to analyze the level of expected RoR volatility as an initial step to see and predict the stability of the fluctuations in the rate of return of Indonesian Islamic financing instruments.Methods – Probability of Occurence, Expected Rate of Return (RoR and GARCH (Generalized Autoregressive Conditional Heteroscedasticity.Results - The expected RoR volatility of the murabaha and istishna financing instruments tend to be more volatile than expected RoR volatility of musharaka and qardh financing instruments
The Integration Order of Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We show that the order of integration of a vector autoregressive process is equal to the difference between the multiplicity of the unit root in the characteristic equation and the multiplicity of the unit root in the adjoint matrix polynomial. The equivalence with the standard I(1) and I(2...
Monthly streamflow forecasting with auto-regressive integrated moving average
Nasir, Najah; Samsudin, Ruhaidah; Shabri, Ani
2017-09-01
Forecasting of streamflow is one of the many ways that can contribute to better decision making for water resource management. The auto-regressive integrated moving average (ARIMA) model was selected in this research for monthly streamflow forecasting with enhancement made by pre-processing the data using singular spectrum analysis (SSA). This study also proposed an extension of the SSA technique to include a step where clustering was performed on the eigenvector pairs before reconstruction of the time series. The monthly streamflow data of Sungai Muda at Jeniang, Sungai Muda at Jambatan Syed Omar and Sungai Ketil at Kuala Pegang was gathered from the Department of Irrigation and Drainage Malaysia. A ratio of 9:1 was used to divide the data into training and testing sets. The ARIMA, SSA-ARIMA and Clustered SSA-ARIMA models were all developed in R software. Results from the proposed model are then compared to a conventional auto-regressive integrated moving average model using the root-mean-square error and mean absolute error values. It was found that the proposed model can outperform the conventional model.
Methodology for the AutoRegressive Planet Search (ARPS) Project
Feigelson, Eric; Caceres, Gabriel; ARPS Collaboration
2018-01-01
The detection of periodic signals of transiting exoplanets is often impeded by the presence of aperiodic photometric variations. This variability is intrinsic to the host star in space-based observations (typically arising from magnetic activity) and from observational conditions in ground-based observations. The most common statistical procedures to remove stellar variations are nonparametric, such as wavelet decomposition or Gaussian Processes regression. However, many stars display variability with autoregressive properties, wherein later flux values are correlated with previous ones. Providing the time series is evenly spaced, parametric autoregressive models can prove very effective. Here we present the methodology of the Autoregessive Planet Search (ARPS) project which uses Autoregressive Integrated Moving Average (ARIMA) models to treat a wide variety of stochastic short-memory processes, as well as nonstationarity. Additionally, we introduce a planet-search algorithm to detect periodic transits in the time-series residuals after application of ARIMA models. Our matched-filter algorithm, the Transit Comb Filter (TCF), replaces the traditional box-fitting step. We construct a periodogram based on the TCF to concentrate the signal of these periodic spikes. Various features of the original light curves, the ARIMA fits, the TCF periodograms, and folded light curves at peaks of the TCF periodogram can then be collected to provide constraints for planet detection. These features provide input into a multivariate classifier when a training set is available. The ARPS procedure has been applied NASA's Kepler mission observations of ~200,000 stars (Caceres, Dissertation Talk, this meeting) and will be applied in the future to other datasets.
Temporal aggregation in first order cointegrated vector autoregressive
DEFF Research Database (Denmark)
la Cour, Lisbeth Funding; Milhøj, Anders
2006-01-01
We study aggregation - or sample frequencies - of time series, e.g. aggregation from weekly to monthly or quarterly time series. Aggregation usually gives shorter time series but spurious phenomena, in e.g. daily observations, can on the other hand be avoided. An important issue is the effect of ...... of aggregation on the adjustment coefficient in cointegrated systems. We study only first order vector autoregressive processes for n dimensional time series Xt, and we illustrate the theory by a two dimensional and a four dimensional model for prices of various grades of gasoline....
International Nuclear Information System (INIS)
Shipler, D.B.; Napier, B.A.
1992-07-01
This report details the conceptual approaches to be used in calculating radiation doses to individuals throughout the various periods of operations at the Hanford Site. The report considers the major environmental transport pathways--atmospheric, surface water, and ground water--and projects and appropriate modeling technique for each. The modeling sequence chosen for each pathway depends on the available data on doses, the degree of confidence justified by such existing data, and the level of sophistication deemed appropriate for the particular pathway and time period being considered
Multistage Stochastic Programming via Autoregressive Sequences
Czech Academy of Sciences Publication Activity Database
Kaňková, Vlasta
2007-01-01
Roč. 15, č. 4 (2007), s. 99-110 ISSN 0572-3043 R&D Projects: GA ČR GA402/07/1113; GA ČR(CZ) GA402/06/0990; GA ČR GD402/03/H057 Institutional research plan: CEZ:AV0Z10750506 Keywords : Economic proceses * Multistage stochastic programming * autoregressive sequences * individual probability constraints Subject RIV: BB - Applied Statistics, Operational Research
Folmer, Eelke O.; Olff, Han; Piersma, Theunis; Robinson, Rob
Patch choice of foraging animals is typically assumed to depend positively on food availability and negatively on interference while benefits of the co-occurrence of conspecifics tend to be ignored. In this paper we integrate a classical functional response model based on resource availability and
Autoregressive Processes in Homogenization of GNSS Tropospheric Data
Klos, A.; Bogusz, J.; Teferle, F. N.; Bock, O.; Pottiaux, E.; Van Malderen, R.
2016-12-01
Offsets due to changes in hardware equipment or any other artificial event are all a subject of a task of homogenization of tropospheric data estimated within a processing of Global Navigation Satellite System (GNSS) observables. This task is aimed at identifying exact epochs of offsets and estimate their magnitudes since they may artificially under- or over-estimate trend and its uncertainty delivered from tropospheric data and used in climate studies. In this research, we analysed a common data set of differences of Integrated Water Vapour (IWV) from GPS and ERA-Interim (1995-2010) provided for a homogenization group working within ES1206 COST Action GNSS4SWEC. We analysed daily IWV records of GPS and ERA-Interim in terms of trend, seasonal terms and noise model with Maximum Likelihood Estimation in Hector software. We found that this data has a character of autoregressive process (AR). Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different noise types: white as well as combination of white and autoregressive and also added few strictly defined offsets. This synthetic data set of exactly the same character as IWV from GPS and ERA-Interim was then subjected to a task of manual and automatic/statistical homogenization. We made blind tests and detected possible epochs of offsets manually. We found that simulated offsets were easily detected in series with white noise, no influence of seasonal signal was noticed. The autoregressive series were much more problematic when offsets had to be determined. We found few epochs, for which no offset was simulated. This was mainly due to strong autocorrelation of data, which brings an artificial trend within. Due to regime-like behaviour of AR it is difficult for statistical methods to properly detect epochs of offsets, which was previously reported by climatologists.
Energy Technology Data Exchange (ETDEWEB)
Castro, Jorge Henrique de [Petroleo Brasileiro S.A. (PETROBRAS), Rio de Janeiro, RJ (Brazil); Silva, Alexandre Pinto Alves da [Coordenacao dos Programas de Pos-Graduacao de Engenharia (COPPE/UFRJ), RJ (Brazil). Programa de Engenharia Eletrica
2010-07-01
Develop the natural gas network is critical success factor for the distribution company. It is a decision that employs the demand given location 'x' and a future time 't' so that the net allows the best conditions for the return of the capital. In this segment, typical network industry, the spatial infra-structure vision associated to the market allows better evaluation of the business because to mitigate costs and risks. In fact, economic models little developed in order to assess the question of the location, due to its little employment by economists. The objective of this article is to analyze the application of spatial perspective in natural gas demand forecasting and to identify the models that can be employed observing issues of dependency and spatial heterogeneity; as well as the capacity of mapping of variables associated with the problem. (author)
Stock Market Autoregressive Dynamics: A Multinational Comparative Study with Quantile Regression
Directory of Open Access Journals (Sweden)
Lili Li
2016-01-01
Full Text Available We study the nonlinear autoregressive dynamics of stock index returns in seven major advanced economies (G7 and China. The quantile autoregression model (QAR enables us to investigate the autocorrelation across the whole spectrum of return distribution, which provides more insightful conditional information on multinational stock market dynamics than conventional time series models. The relation between index return and contemporaneous trading volume is also investigated. While prior studies have mixed results on stock market autocorrelations, we find that the dynamics is usually state dependent. The results for G7 stock markets exhibit conspicuous similarities, but they are in manifest contrast to the findings on Chinese stock markets.
THE ALLOMETRIC-AUTOREGRESSIVE MODEL IN GENETIC ...
African Journals Online (AJOL)
x(t) = In (cumulative feed intake) at time t, x(O) = In (cumulative feed intake) at time O. where w = body mass and v = cumulative feed in- take (Roux, 1976). Slope (b) and intercept (In a) can thus be estimated by linear least squares pro- cedures. Part of MSc (Agric) thesis of the senior author sub- mitted to the University of the ...
Multivariate autoregressive algorithms for ocean wave modelling
Digital Repository Service at National Institute of Oceanography (India)
Mandal, S.; Lyons, G.J.; Witz, J.A.
stream_size 8 stream_content_type text/plain stream_name 2_Int_Offshore_Polar_Eng_Conf_Proc_1992_77.pdf.txt stream_source_info 2_Int_Offshore_Polar_Eng_Conf_Proc_1992_77.pdf.txt Content-Encoding ISO-8859-1 Content-Type text...
A Fault Diagnosis Approach for Gears Based on IMF AR Model and SVM
Directory of Open Access Journals (Sweden)
Yu Yang
2008-05-01
Full Text Available An accurate autoregressive (AR model can reflect the characteristics of a dynamic system based on which the fault feature of gear vibration signal can be extracted without constructing mathematical model and studying the fault mechanism of gear vibration system, which are experienced by the time-frequency analysis methods. However, AR model can only be applied to stationary signals, while the gear fault vibration signals usually present nonstationary characteristics. Therefore, empirical mode decomposition (EMD, which can decompose the vibration signal into a finite number of intrinsic mode functions (IMFs, is introduced into feature extraction of gear vibration signals as a preprocessor before AR models are generated. On the other hand, by targeting the difficulties of obtaining sufficient fault samples in practice, support vector machine (SVM is introduced into gear fault pattern recognition. In the proposed method in this paper, firstly, vibration signals are decomposed into a finite number of intrinsic mode functions, then the AR model of each IMF component is established; finally, the corresponding autoregressive parameters and the variance of remnant are regarded as the fault characteristic vectors and used as input parameters of SVM classifier to classify the working condition of gears. The experimental analysis results show that the proposed approach, in which IMF AR model and SVM are combined, can identify working condition of gears with a success rate of 100% even in the case of smaller number of samples.
Bayesian modeling and prediction of solar particles flux
International Nuclear Information System (INIS)
Dedecius, Kamil; Kalova, Jana
2010-01-01
An autoregression model was developed based on the Bayesian approach. Considering the solar wind non-homogeneity, the idea was applied of combining the pure autoregressive properties of the model with expert knowledge based on a similar behaviour of the various phenomena related to the flux properties. Examples of such situations include the hardening of the X-ray spectrum, which is often followed by coronal mass ejection and a significant increase in the particles flux intensity
An algebraic method for constructing stable and consistent autoregressive filters
International Nuclear Information System (INIS)
Harlim, John; Hong, Hoon; Robbins, Jacob L.
2015-01-01
In this paper, we introduce an algebraic method to construct stable and consistent univariate autoregressive (AR) models of low order for filtering and predicting nonlinear turbulent signals with memory depth. By stable, we refer to the classical stability condition for the AR model. By consistent, we refer to the classical consistency constraints of Adams–Bashforth methods of order-two. One attractive feature of this algebraic method is that the model parameters can be obtained without directly knowing any training data set as opposed to many standard, regression-based parameterization methods. It takes only long-time average statistics as inputs. The proposed method provides a discretization time step interval which guarantees the existence of stable and consistent AR model and simultaneously produces the parameters for the AR models. In our numerical examples with two chaotic time series with different characteristics of decaying time scales, we find that the proposed AR models produce significantly more accurate short-term predictive skill and comparable filtering skill relative to the linear regression-based AR models. These encouraging results are robust across wide ranges of discretization times, observation times, and observation noise variances. Finally, we also find that the proposed model produces an improved short-time prediction relative to the linear regression-based AR-models in forecasting a data set that characterizes the variability of the Madden–Julian Oscillation, a dominant tropical atmospheric wave pattern
Biometeorological and autoregressive indices for predicting olive pollen intensity.
Oteros, J; García-Mozo, H; Hervás, C; Galán, C
2013-03-01
This paper reports on modelling to predict airborne olive pollen season severity, expressed as a pollen index (PI), in Córdoba province (southern Spain) several weeks prior to the pollen season start. Using a 29-year database (1982-2010), a multivariate regression model based on five indices-the index-based model-was built to enhance the efficacy of prediction models. Four of the indices used were biometeorological indices: thermal index, pre-flowering hydric index, dormancy hydric index and summer index; the fifth was an autoregressive cyclicity index based on pollen data from previous years. The extreme weather events characteristic of the Mediterranean climate were also taken into account by applying different adjustment criteria. The results obtained with this model were compared with those yielded by a traditional meteorological-based model built using multivariate regression analysis of simple meteorological-related variables. The performance of the models (confidence intervals, significance levels and standard errors) was compared, and they were also validated using the bootstrap method. The index-based model built on biometeorological and cyclicity indices was found to perform better for olive pollen forecasting purposes than the traditional meteorological-based model.
Material Modelling - Composite Approach
DEFF Research Database (Denmark)
Nielsen, Lauge Fuglsang
1997-01-01
is successfully justified comparing predicted results with experimental data obtained in the HETEK-project on creep, relaxation, and shrinkage of very young concretes cured at a temperature of T = 20^o C and a relative humidity of RH = 100%. The model is also justified comparing predicted creep, shrinkage......, and internal stresses caused by drying shrinkage with experimental results reported in the literature on the mechanical behavior of mature concretes. It is then concluded that the model presented applied in general with respect to age at loading.From a stress analysis point of view the most important finding...... in this report is that cement paste and concrete behave practically as linear-viscoelastic materials from an age of approximately 10 hours. This is a significant age extension relative to earlier studies in the literature where linear-viscoelastic behavior is only demonstrated from ages of a few days. Thus...
On robust forecasting of autoregressive time series under censoring
Kharin, Y.; Badziahin, I.
2009-01-01
Problems of robust statistical forecasting are considered for autoregressive time series observed under distortions generated by interval censoring. Three types of robust forecasting statistics are developed; meansquare risk is evaluated for the developed forecasting statistics. Numerical results are given.
Directory of Open Access Journals (Sweden)
Siti Choirun Nisak
2016-06-01
Full Text Available Time series forecasting models can be used to predict phenomena that occur in nature. Generalized Space Time Autoregressive (GSTAR is one of time series model used to forecast the data consisting the elements of time and space. This model is limited to the stationary and non-seasonal data. Generalized Space Time Autoregressive Integrated Moving Average (GSTARIMA is GSTAR development model that accommodates the non-stationary and seasonal data. Ordinary Least Squares (OLS is method used to estimate parameter of GSTARIMA model. Estimation parameter of GSTARIMA model using OLS will not produce efficiently estimator if there is an error correlation between spaces. Ordinary Least Square (OLS assumes the variance-covariance matrix has a constant error ~(, but in fact, the observatory spaces are correlated so that variance-covariance matrix of the error is not constant. Therefore, Seemingly Unrelated Regression (SUR approach is used to accommodate the weakness of the OLS. SUR assumption is ~(, for estimating parameters GSTARIMA model. The method to estimate parameter of SUR is Generalized Least Square (GLS. Applications GSTARIMA-SUR models for rainfall data in the region Malang obtained GSTARIMA models ((1(1,12,36,(0,(1-SUR with determination coefficient generated with the average of 57.726%.
Modelling Chinese Inbound Tourism Arrivals into Christchurch
Fieger, Peter; Rice, John
2016-01-01
New data and modelling approaches are improving the usefulness of internet search data for forecasting inbound tourist arrivals. This short paper provides evidence of the usefulness of Baidu search data in predicting Chinese inbound tourist arrivals into a specific region in New Zealand. It also compares three modelling approaches, finding a Vector Autoregressive approach the most useful.
A MODEL FOR THE PALM OIL MARKET IN NIGERIA: AN ECONOMETRICS APPROACH
Directory of Open Access Journals (Sweden)
Henry Egwuma
2016-04-01
Full Text Available The aim of this study is to formulate and estimate a model for the palm oil market in Nigeria with a view to identifying principal factors that shape the Nigerian palm oil industry. Four structural equation models comprising palm oil production, import demand, domestic demand and producer price have been estimated using the autoregressive distributed lag (ARDL cointegration approach over the 1970 to 2011 period. The results reveal that significant factors that influence the Nigerian palm oil industry include the own price, technological improvements, and income level. Government expenditure on agricultural development is also an important determinant, which underscores the need for government support in agriculture. Our model provides a useful framework for analyzing the effects of changes in major exogenous variables such as income or import tariff on the production, demand, and price of palm oil.
Autoregressive Prediction with Rolling Mechanism for Time Series Forecasting with Small Sample Size
Directory of Open Access Journals (Sweden)
Zhihua Wang
2014-01-01
Full Text Available Reasonable prediction makes significant practical sense to stochastic and unstable time series analysis with small or limited sample size. Motivated by the rolling idea in grey theory and the practical relevance of very short-term forecasting or 1-step-ahead prediction, a novel autoregressive (AR prediction approach with rolling mechanism is proposed. In the modeling procedure, a new developed AR equation, which can be used to model nonstationary time series, is constructed in each prediction step. Meanwhile, the data window, for the next step ahead forecasting, rolls on by adding the most recent derived prediction result while deleting the first value of the former used sample data set. This rolling mechanism is an efficient technique for its advantages of improved forecasting accuracy, applicability in the case of limited and unstable data situations, and requirement of little computational effort. The general performance, influence of sample size, nonlinearity dynamic mechanism, and significance of the observed trends, as well as innovation variance, are illustrated and verified with Monte Carlo simulations. The proposed methodology is then applied to several practical data sets, including multiple building settlement sequences and two economic series.
Use of (Time-Domain) Vector Autoregressions to Test Uncovered Interest Parity
Takatoshi Ito
1984-01-01
In this paper, a vector autoregression model (VAR) is proposed in order to test uncovered interest parity (UIP) in the foreign exchange market. Consider a VAR system of the spot exchange rate (yen/dollar), the domestic (US) interest rate and the foreign (Japanese) interest rate, describing the interdependence of the domestic and international financia lmarkets. Uncovered interest parity is stated as a null hypothesis that the current difference between the two interest rates is equal to the d...
Exchange rate pass-through in Switzerland: Evidence from vector autoregressions
Jonas Stulz
2007-01-01
This study investigates the pass-through of exchange rate and import price shocks to different aggregated prices in Switzerland. The baseline analysis is carried out with recursively identified vector autoregressive (VAR) models. The data set comprises monthly observations, and pass-through effects are quantified by means of impulse response functions. Evidence shows that the exchange rate pass-through to import prices is substantial (although incomplete), but only moderate to total consumer ...
Evaporator modeling - A hybrid approach
International Nuclear Information System (INIS)
Ding Xudong; Cai Wenjian; Jia Lei; Wen Changyun
2009-01-01
In this paper, a hybrid modeling approach is proposed to model two-phase flow evaporators. The main procedures for hybrid modeling includes: (1) Based on the energy and material balance, and thermodynamic principles to formulate the process fundamental governing equations; (2) Select input/output (I/O) variables responsible to the system performance which can be measured and controlled; (3) Represent those variables existing in the original equations but are not measurable as simple functions of selected I/Os or constants; (4) Obtaining a single equation which can correlate system inputs and outputs; and (5) Identify unknown parameters by linear or nonlinear least-squares methods. The method takes advantages of both physical and empirical modeling approaches and can accurately predict performance in wide operating range and in real-time, which can significantly reduce the computational burden and increase the prediction accuracy. The model is verified with the experimental data taken from a testing system. The testing results show that the proposed model can predict accurately the performance of the real-time operating evaporator with the maximum error of ±8%. The developed models will have wide applications in operational optimization, performance assessment, fault detection and diagnosis
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan
2017-11-20
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Non-Gaussian Autoregressive Processes with Tukey g-and-h Transformations
Yan, Yuan; Genton, Marc G.
2017-01-01
When performing a time series analysis of continuous data, for example from climate or environmental problems, the assumption that the process is Gaussian is often violated. Therefore, we introduce two non-Gaussian autoregressive time series models that are able to fit skewed and heavy-tailed time series data. Our two models are based on the Tukey g-and-h transformation. We discuss parameter estimation, order selection, and forecasting procedures for our models and examine their performances in a simulation study. We demonstrate the usefulness of our models by applying them to two sets of wind speed data.
Chan, Jennifer S K
2016-05-01
Dropouts are common in longitudinal study. If the dropout probability depends on the missing observations at or after dropout, this type of dropout is called informative (or nonignorable) dropout (ID). Failure to accommodate such dropout mechanism into the model will bias the parameter estimates. We propose a conditional autoregressive model for longitudinal binary data with an ID model such that the probabilities of positive outcomes as well as the drop-out indicator in each occasion are logit linear in some covariates and outcomes. This model adopting a marginal model for outcomes and a conditional model for dropouts is called a selection model. To allow for the heterogeneity and clustering effects, the outcome model is extended to incorporate mixture and random effects. Lastly, the model is further extended to a novel model that models the outcome and dropout jointly such that their dependency is formulated through an odds ratio function. Parameters are estimated by a Bayesian approach implemented using the user-friendly Bayesian software WinBUGS. A methadone clinic dataset is analyzed to illustrate the proposed models. Result shows that the treatment time effect is still significant but weaker after allowing for an ID process in the data. Finally the effect of drop-out on parameter estimates is evaluated through simulation studies. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.
Dharmasena, Senarath; Capps, Oral, Jr.; Bessler, David A.
2012-01-01
The non-alcoholic beverage market in the U.S. is a multi-billion dollar industry growing steadily over the past decade. Also, non-alcoholic beverages are among the most heavily advertised food and beverage groups in the United States. Several studies pertaining to non-alcoholic beverages including the incorporation of advertising effects have been conducted, but most of these have centered attention on milk consumption. Some studies have considered demand interrelationships for several bevera...
Estimation bias and bias correction in reduced rank autoregressions
DEFF Research Database (Denmark)
Nielsen, Heino Bohn
2017-01-01
This paper characterizes the finite-sample bias of the maximum likelihood estimator (MLE) in a reduced rank vector autoregression and suggests two simulation-based bias corrections. One is a simple bootstrap implementation that approximates the bias at the MLE. The other is an iterative root...
A General Representation Theorem for Integrated Vector Autoregressive Processes
DEFF Research Database (Denmark)
Franchi, Massimo
We study the algebraic structure of an I(d) vector autoregressive process, where d is restricted to be an integer. This is useful to characterize its polynomial cointegrating relations and its moving average representation, that is to prove a version of the Granger representation theorem valid...
HEDR modeling approach: Revision 1
International Nuclear Information System (INIS)
Shipler, D.B.; Napier, B.A.
1994-05-01
This report is a revision of the previous Hanford Environmental Dose Reconstruction (HEDR) Project modeling approach report. This revised report describes the methods used in performing scoping studies and estimating final radiation doses to real and representative individuals who lived in the vicinity of the Hanford Site. The scoping studies and dose estimates pertain to various environmental pathways during various periods of time. The original report discussed the concepts under consideration in 1991. The methods for estimating dose have been refined as understanding of existing data, the scope of pathways, and the magnitudes of dose estimates were evaluated through scoping studies
Forecasting autoregressive time series under changing persistence
DEFF Research Database (Denmark)
Kruse, Robinson
Changing persistence in time series models means that a structural change from nonstationarity to stationarity or vice versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model predictions. This paper derives generally applicable...
A nonlinear approach to modelling the residential electricity consumption in Ethiopia
International Nuclear Information System (INIS)
Gabreyohannes, Emmanuel
2010-01-01
In this paper an attempt is made to model, analyze and forecast the residential electricity consumption in Ethiopia using the self-exciting threshold autoregressive (SETAR) model and the smooth transition regression (STR) model. For comparison purposes, the application was also extended to standard linear models. During the empirical presentation of both models, significant nonlinear effects were found and linearity was rejected. The SETAR model was found out to be relatively better than the linear autoregressive model in out-of-sample point and interval (density) forecasts. Results from our STR model showed that the residual variance of the fitted STR model was only about 65.7% of that of the linear ARX model. Thus, we can conclude that the inclusion of the nonlinear part, which basically accounts for the arrival of extreme price events, leads to improvements in the explanatory abilities of the model for electricity consumption in Ethiopia. (author)
A nonlinear approach to modelling the residential electricity consumption in Ethiopia
Energy Technology Data Exchange (ETDEWEB)
Gabreyohannes, Emmanuel [Ethiopian Civil Service College, P.O.Box 5648, Addis Ababa (Ethiopia)
2010-05-15
In this paper an attempt is made to model, analyze and forecast the residential electricity consumption in Ethiopia using the self-exciting threshold autoregressive (SETAR) model and the smooth transition regression (STR) model. For comparison purposes, the application was also extended to standard linear models. During the empirical presentation of both models, significant nonlinear effects were found and linearity was rejected. The SETAR model was found out to be relatively better than the linear autoregressive model in out-of-sample point and interval (density) forecasts. Results from our STR model showed that the residual variance of the fitted STR model was only about 65.7% of that of the linear ARX model. Thus, we can conclude that the inclusion of the nonlinear part, which basically accounts for the arrival of extreme price events, leads to improvements in the explanatory abilities of the model for electricity consumption in Ethiopia. (author)
An SEM Approach to Continuous Time Modeling of Panel Data: Relating Authoritarianism and Anomia
Voelkle, Manuel C.; Oud, Johan H. L.; Davidov, Eldad; Schmidt, Peter
2012-01-01
Panel studies, in which the same subjects are repeatedly observed at multiple time points, are among the most popular longitudinal designs in psychology. Meanwhile, there exists a wide range of different methods to analyze such data, with autoregressive and cross-lagged models being 2 of the most well known representatives. Unfortunately, in these…
Forecasting exchange rates: a robust regression approach
Preminger, Arie; Franck, Raphael
2005-01-01
The least squares estimation method as well as other ordinary estimation method for regression models can be severely affected by a small number of outliers, thus providing poor out-of-sample forecasts. This paper suggests a robust regression approach, based on the S-estimation method, to construct forecasting models that are less sensitive to data contamination by outliers. A robust linear autoregressive (RAR) and a robust neural network (RNN) models are estimated to study the predictabil...
Characterization of autoregressive processes using entropic quantifiers
Traversaro, Francisco; Redelico, Francisco O.
2018-01-01
The aim of the contribution is to introduce a novel information plane, the causal-amplitude informational plane. As previous works seems to indicate, Bandt and Pompe methodology for estimating entropy does not allow to distinguish between probability distributions which could be fundamental for simulation or for probability analysis purposes. Once a time series is identified as stochastic by the causal complexity-entropy informational plane, the novel causal-amplitude gives a deeper understanding of the time series, quantifying both, the autocorrelation strength and the probability distribution of the data extracted from the generating processes. Two examples are presented, one from climate change model and the other from financial markets.
Pachhai, S.; Masters, G.; Laske, G.
2017-12-01
Earth's normal-mode spectra are crucial to studying the long wavelength structure of the Earth. Such observations have been used extensively to estimate "splitting coefficients" which, in turn, can be used to determine the three-dimensional velocity and density structure. Most past studies apply a non-linear iterative inversion to estimate the splitting coefficients which requires that the earthquake source is known. However, it is challenging to know the source details, particularly for big events as used in normal-mode analyses. Additionally, the final solution of the non-linear inversion can depend on the choice of damping parameter and starting model. To circumvent the need to know the source, a two-step linear inversion has been developed and successfully applied to many mantle and core sensitive modes. The first step takes combinations of the data from a single event to produce spectra known as "receiver strips". The autoregressive nature of the receiver strips can then be used to estimate the structure coefficients without the need to know the source. Based on this approach, we recently employed a neighborhood algorithm to measure the splitting coefficients for an isolated inner-core sensitive mode (13S2). This approach explores the parameter space efficiently without any need of regularization and finds the structure coefficients which best fit the observed strips. Here, we implement a Bayesian approach to data collected for earthquakes from early 2000 and more recent. This approach combines the data (through likelihood) and prior information to provide rigorous parameter values and their uncertainties for both isolated and coupled modes. The likelihood function is derived from the inferred errors of the receiver strips which allows us to retrieve proper uncertainties. Finally, we apply model selection criteria that balance the trade-offs between fit (likelihood) and model complexity to investigate the degree and type of structure (elastic and anelastic
International Nuclear Information System (INIS)
Aruquipa Coloma, Wilmer
2017-01-01
Nuclear reactors are susceptible to instability, causing oscillations in reactor power in specific working regions characterized by determined values of power and coolant mass flow. During reactor startup, there is a greater probability that these regions of instability will be present; another reason may be due to transient processes in some reactor parameters. The analysis of the temporal evolution of the power reveals a stable or unstable process after the disturbance in a light water reactor of type BWR (Boiling Water Reactor). In this work, the instability problem was approached in two ways. The first form is based on the ARMA (Autoregressive Moving Average models) model. This model was used to calculate the Decay Ratio (DR) and natural frequency (NF) of the oscillations, parameters that indicate if the one power signal is stable or not. In this sense, the DRARMA code was developed. In the second form, the problems of instability were analyzed using the classical concepts of non-linear systems, such as Lyapunov exponents, phase space and attractors. The Lyapunov exponents quantify the exponential divergence of the trajectories initially close to the phase space and estimate the amount of chaos in a system; the phase space and the attractors describe the dynamic behavior of the system. The main aim of the instability phenomena studies in nuclear reactors is to try to identify points or regions of operation that can lead to power oscillations conditions. The two approaches were applied to two sets of signals. The first set comes from signals of instability events of the commercial Forsmark reactors 1 and 2 and were used to validate the DRARMA code. The second set was obtained from the simulation of transient events of the Peach Bottom reactor; for the simulation, the PARCS and RELAP5 codes were used for the neutronic/thermal hydraulic coupling calculation. For all analyzes made in this work, the Matlab software was used due to its ease of programming and
International Nuclear Information System (INIS)
Benth, Fred Espen; Taib, Che Mohd Imran Che
2013-01-01
We extend the concept of half life of an Ornstein–Uhlenbeck process to Lévy-driven continuous-time autoregressive moving average processes with stochastic volatility. The half life becomes state dependent, and we analyze its properties in terms of the characteristics of the process. An empirical example based on daily temperatures observed in Petaling Jaya, Malaysia, is presented, where the proposed model is estimated and the distribution of the half life is simulated. The stationarity of the dynamics yield futures prices which asymptotically tend to constant at an exponential rate when time to maturity goes to infinity. The rate is characterized by the eigenvalues of the dynamics. An alternative description of this convergence can be given in terms of our concept of half life. - Highlights: • The concept of half life is extended to Levy-driven continuous time autoregressive moving average processes • The dynamics of Malaysian temperatures are modeled using a continuous time autoregressive model with stochastic volatility • Forward prices on temperature become constant when time to maturity tends to infinity • Convergence in time to maturity is at an exponential rate given by the eigenvalues of the model temperature model
A Bayesian approach to model uncertainty
International Nuclear Information System (INIS)
Buslik, A.
1994-01-01
A Bayesian approach to model uncertainty is taken. For the case of a finite number of alternative models, the model uncertainty is equivalent to parameter uncertainty. A derivation based on Savage's partition problem is given
Kalkkuhl, J; Hunt, K J; Fritz, H
1999-01-01
An finite-element methods (FEM)-based neural-network approach to Nonlinear AutoRegressive with eXogenous input (NARX) modeling is presented. The method uses multilinear interpolation functions on C0 rectangular elements. The local and global structure of the resulting model is analyzed. It is shown that the model can be interpreted both as a local model network and a single layer feedforward neural network. The main aim is to use the model for nonlinear control design. The proposed FEM NARX description is easily accessible to feedback linearizing control techniques. Its use with a two-degrees of freedom nonlinear internal model controller is discussed. The approach is applied to modeling of the nonlinear longitudinal dynamics of an experimental lorry, using measured data. The modeling results are compared with local model network and multilayer perceptron approaches. A nonlinear speed controller was designed based on the identified FEM model. The controller was implemented in a test vehicle, and several experimental results are presented.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
International Nuclear Information System (INIS)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-01-01
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan
2014-09-01
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
Medium term municipal solid waste generation prediction by autoregressive integrated moving average
Energy Technology Data Exchange (ETDEWEB)
Younes, Mohammad K.; Nopiah, Z. M.; Basri, Noor Ezlin A.; Basri, Hassan [Department of Civil and Structural Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor (Malaysia)
2014-09-12
Generally, solid waste handling and management are performed by municipality or local authority. In most of developing countries, local authorities suffer from serious solid waste management (SWM) problems and insufficient data and strategic planning. Thus it is important to develop robust solid waste generation forecasting model. It helps to proper manage the generated solid waste and to develop future plan based on relatively accurate figures. In Malaysia, solid waste generation rate increases rapidly due to the population growth and new consumption trends that characterize the modern life style. This paper aims to develop monthly solid waste forecasting model using Autoregressive Integrated Moving Average (ARIMA), such model is applicable even though there is lack of data and will help the municipality properly establish the annual service plan. The results show that ARIMA (6,1,0) model predicts monthly municipal solid waste generation with root mean square error equals to 0.0952 and the model forecast residuals are within accepted 95% confident interval.
System Behavior Models: A Survey of Approaches
2016-06-01
OF FIGURES Spiral Model .................................................................................................3 Figure 1. Approaches in... spiral model was chosen for researching and structuring this thesis, shown in Figure 1. This approach allowed multiple iterations of source material...applications and refining through iteration. 3 Spiral Model Figure 1. D. SCOPE The research is limited to a literature review, limited
Learning Actions Models: Qualitative Approach
DEFF Research Database (Denmark)
Bolander, Thomas; Gierasimczuk, Nina
2015-01-01
In dynamic epistemic logic, actions are described using action models. In this paper we introduce a framework for studying learnability of action models from observations. We present first results concerning propositional action models. First we check two basic learnability criteria: finite ident...
Time Series ARIMA Models of Undergraduate Grade Point Average.
Rogers, Bruce G.
The Auto-Regressive Integrated Moving Average (ARIMA) Models, often referred to as Box-Jenkins models, are regression methods for analyzing sequential dependent observations with large amounts of data. The Box-Jenkins approach, a three-stage procedure consisting of identification, estimation and diagnosis, was used to select the most appropriate…
SEM based CARMA time series modeling for arbitrary N
Oud, J.H.L.; Völkle, M.C.; Driver, C.C.
2018-01-01
This article explains in detail the state space specification and estimation of first and higher-order autoregressive moving-average models in continuous time (CARMA) in an extended structural equation modeling (SEM) context for N = 1 as well as N > 1. To illustrate the approach, simulations will be
A combined modeling approach for wind power feed-in and electricity spot prices
International Nuclear Information System (INIS)
Keles, Dogan; Genoese, Massimo; Möst, Dominik; Ortlieb, Sebastian; Fichtner, Wolf
2013-01-01
Wind power generation and its impacts on electricity prices has strongly increased in the EU. Therefore, appropriate mark-to-market evaluation of new investments in wind power and energy storage plants should consider the fluctuant generation of wind power and uncertain electricity prices, which are affected by wind power feed-in (WPF). To gain the input data for WPF and electricity prices, simulation models, such as econometric models, can serve as a data basis. This paper describes a combined modeling approach for the simulation of WPF series and electricity prices considering the impacts of WPF on prices based on an autoregressive approach. Thereby WPF series are firstly simulated for each hour of the year and integrated in the electricity price model to generate an hourly resolved price series for a year. The model results demonstrate that the WPF model delivers satisfying WPF series and that the extended electricity price model considering WPF leads to a significant improvement of the electricity price simulation compared to a model version without WPF effects. As the simulated series of WPF and electricity prices also contain the correlation between both series, market evaluation of wind power technologies can be accurately done based on these series. - Highlights: • Wind power feed-in can be directly simulated with stochastic processes. • Non-linear relationship between wind power feed-in and electricity prices. • Price reduction effect of wind power feed-in depends on the actual load. • Considering wind power feed-in effects improves the electricity price simulation. • Combined modeling of both parameters delivers a data basis for evaluation tools
METODE VECTOR AUTOREGRESSIVE (VAR DALAM PERAMALAN JUMLAH WISATAWAN MANCANEGARA KE BALI
Directory of Open Access Journals (Sweden)
TJOK GDE SAHITYAHUTTI RANANGGA
2018-05-01
Full Text Available The purposes of this research were to model and to forecast the number of foreign tourists (Australia, China, and Japan arrival to Bali using vector autoregressive (VAR method. The estimated of VAR model obtained to forecast the number of foreign tourists to Bali is the sixth order VAR (VAR(6.We used multivariate least square method to estimate the VAR(6’s parameters.The mean absolute percentage error (MAPE in this model were as follows 6.8% in predicting the number of Australian tourists, 15.9% in predicting the number of Chinese tourists, and 9% in predicting the number of Japanese tourists. The prediction of Australian, Chinese, and Japanese tourists arrival to Bali for July 2017 to December 2017 tended to experience up and downs that were not too high compared to the previous months.
Very-short-term wind power probabilistic forecasts by sparse vector autoregression
DEFF Research Database (Denmark)
Dowell, Jethro; Pinson, Pierre
2016-01-01
A spatio-temporal method for producing very-shortterm parametric probabilistic wind power forecasts at a large number of locations is presented. Smart grids containing tens, or hundreds, of wind generators require skilled very-short-term forecasts to operate effectively, and spatial information...... is highly desirable. In addition, probabilistic forecasts are widely regarded as necessary for optimal power system management as they quantify the uncertainty associated with point forecasts. Here we work within a parametric framework based on the logit-normal distribution and forecast its parameters....... The location parameter for multiple wind farms is modelled as a vector-valued spatiotemporal process, and the scale parameter is tracked by modified exponential smoothing. A state-of-the-art technique for fitting sparse vector autoregressive models is employed to model the location parameter and demonstrates...
Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM
Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman
2012-01-01
This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…
Global energy modeling - A biophysical approach
Energy Technology Data Exchange (ETDEWEB)
Dale, Michael
2010-09-15
This paper contrasts the standard economic approach to energy modelling with energy models using a biophysical approach. Neither of these approaches includes changing energy-returns-on-investment (EROI) due to declining resource quality or the capital intensive nature of renewable energy sources. Both of these factors will become increasingly important in the future. An extension to the biophysical approach is outlined which encompasses a dynamic EROI function that explicitly incorporates technological learning. The model is used to explore several scenarios of long-term future energy supply especially concerning the global transition to renewable energy sources in the quest for a sustainable energy system.
A Unified Approach to Modeling and Programming
DEFF Research Database (Denmark)
Madsen, Ole Lehrmann; Møller-Pedersen, Birger
2010-01-01
of this paper is to go back to the future and get inspiration from SIMULA and propose a unied approach. In addition to reintroducing the contributions of SIMULA and the Scandinavian approach to object-oriented programming, we do this by discussing a number of issues in modeling and programming and argue3 why we......SIMULA was a language for modeling and programming and provided a unied approach to modeling and programming in contrast to methodologies based on structured analysis and design. The current development seems to be going in the direction of separation of modeling and programming. The goal...
Packet loss replacement in voip using a recursive low-order autoregressive modelbased speech
International Nuclear Information System (INIS)
Miralavi, Seyed Reza; Ghorshi, Seyed; Mortazavi, Mohammad; Choupan, Jeiran
2011-01-01
In real-time packet-based communication systems one major problem is misrouted or delayed packets which results in degraded perceived voice quality. When some speech packets are not available on time, the packet is known as lost packet in real-time communication systems. The easiest task of a network terminal receiver is to replace silence for the duration of lost speech segments. In a high quality communication system in order to avoid quality reduction due to packet loss a suitable method and/or algorithm is needed to replace the missing segments of speech. In this paper, we introduce a recursive low order autoregressive (AR) model for replacement of lost speech segment. The evaluation results show that this method has a lower mean square error (MSE) and low complexity compared to the other efficient methods like high-order AR model without any substantial degradation in perceived voice quality.
Integer valued autoregressive processes with generalized discrete Mittag-Leffler marginals
Directory of Open Access Journals (Sweden)
Kanichukattu K. Jose
2013-05-01
Full Text Available In this paper we consider a generalization of discrete Mittag-Leffler distributions. We introduce and study the properties of a new distribution called geometric generalized discrete Mittag-Leffler distribution. Autoregressive processes with geometric generalized discrete Mittag-Leffler distributions are developed and studied. The distributions are further extended to develop a more general class of geometric generalized discrete semi-Mittag-Leffler distributions. The processes are extended to higher orders also. An application with respect to an empirical data on customer arrivals in a bank counter is also given. Various areas of potential applications like human resource development, insect growth, epidemic modeling, industrial risk modeling, insurance and actuaries, town planning etc are also discussed.
ECONOMETRIC APPROACH TO DIFFERENCE EQUATIONS MODELING OF EXCHANGE RATES CHANGES
Directory of Open Access Journals (Sweden)
Josip Arnerić
2010-12-01
Full Text Available Time series models that are commonly used in econometric modeling are autoregressive stochastic linear models (AR and models of moving averages (MA. Mentioned models by their structure are actually stochastic difference equations. Therefore, the objective of this paper is to estimate difference equations containing stochastic (random component. Estimated models of time series will be used to forecast observed data in the future. Namely, solutions of difference equations are closely related to conditions of stationary time series models. Based on the fact that volatility is time varying in high frequency data and that periods of high volatility tend to cluster, the most successful and popular models in modeling time varying volatility are GARCH type models and their variants. However, GARCH models will not be analyzed because the purpose of this research is to predict the value of the exchange rate in the levels within conditional mean equation and to determine whether the observed variable has a stable or explosive time path. Based on the estimated difference equation it will be examined whether Croatia is implementing a stable policy of exchange rates.
Multiple Model Approaches to Modelling and Control,
DEFF Research Database (Denmark)
on the ease with which prior knowledge can be incorporated. It is interesting to note that researchers in Control Theory, Neural Networks,Statistics, Artificial Intelligence and Fuzzy Logic have more or less independently developed very similar modelling methods, calling them Local ModelNetworks, Operating......, and allows direct incorporation of high-level and qualitative plant knowledge into themodel. These advantages have proven to be very appealing for industrial applications, and the practical, intuitively appealing nature of the framework isdemonstrated in chapters describing applications of local methods...... to problems in the process industries, biomedical applications and autonomoussystems. The successful application of the ideas to demanding problems is already encouraging, but creative development of the basic framework isneeded to better allow the integration of human knowledge with automated learning...
Geometrical approach to fluid models
International Nuclear Information System (INIS)
Kuvshinov, B.N.; Schep, T.J.
1997-01-01
Differential geometry based upon the Cartan calculus of differential forms is applied to investigate invariant properties of equations that describe the motion of continuous media. The main feature of this approach is that physical quantities are treated as geometrical objects. The geometrical notion of invariance is introduced in terms of Lie derivatives and a general procedure for the construction of local and integral fluid invariants is presented. The solutions of the equations for invariant fields can be written in terms of Lagrange variables. A generalization of the Hamiltonian formalism for finite-dimensional systems to continuous media is proposed. Analogously to finite-dimensional systems, Hamiltonian fluids are introduced as systems that annihilate an exact two-form. It is shown that Euler and ideal, charged fluids satisfy this local definition of a Hamiltonian structure. A new class of scalar invariants of Hamiltonian fluids is constructed that generalizes the invariants that are related with gauge transformations and with symmetries (Noether). copyright 1997 American Institute of Physics
Current approaches to gene regulatory network modelling
Directory of Open Access Journals (Sweden)
Brazma Alvis
2007-09-01
Full Text Available Abstract Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories. We will study the topology of gene regulatory networks in yeast in more detail, comparing a direct network derived from transcription factor binding data and an indirect network derived from genome-wide expression data in mutants. Regarding the network dynamics we briefly describe discrete and continuous approaches to network modelling, then describe a hybrid model called Finite State Linear Model and demonstrate that some simple network dynamics can be simulated in this model.
Asymptotically stable phase synchronization revealed by autoregressive circle maps
Drepper, F. R.
2000-11-01
A specially designed of nonlinear time series analysis is introduced based on phases, which are defined as polar angles in spaces spanned by a finite number of delayed coordinates. A canonical choice of the polar axis and a related implicit estimation scheme for the potentially underlying autoregressive circle map (next phase map) guarantee the invertibility of reconstructed phase space trajectories to the original coordinates. The resulting Fourier approximated, invertibility enforcing phase space map allows us to detect conditional asymptotic stability of coupled phases. This comparatively general synchronization criterion unites two existing generalizations of the old concept and can successfully be applied, e.g., to phases obtained from electrocardiogram and airflow recordings characterizing cardiorespiratory interaction.
Autoregressive techniques for acoustic detection of in-sodium water leaks
International Nuclear Information System (INIS)
Hayashi, K.
1997-01-01
We have been applied a background signal whitening filter built by univariate autoregressive model to the estimation problem of the leak start time and duration. In the 1995 present benchmark stage, we evaluated the method using acoustic signals from real hydrogen or water/steam injection experiments. The results show that the signal processing technique using this filter can detect reliability the leak signals with a sufficient signal-to-noise ratio. Even if the sensor signal contains non-boiling or non-leak high-amplitude pulses, they can be classified by spectral information. Especially, the feature signal made from the time-frequency spectrum of the filtered signal is very sensitive and useful. (author). 8 refs, 14 figs, 6 tabs
Autoregressive Integrated Adaptive Neural Networks Classifier for EEG-P300 Classification
Directory of Open Access Journals (Sweden)
Demi Soetraprawata
2013-06-01
Full Text Available Brain Computer Interface has a potency to be applied in mechatronics apparatus and vehicles in the future. Compared to the other techniques, EEG is the most preferred for BCI designs. In this paper, a new adaptive neural network classifier of different mental activities from EEG-based P300 signals is proposed. To overcome the over-training that is caused by noisy and non-stationary data, the EEG signals are filtered and extracted using autoregressive models before passed to the adaptive neural networks classifier. To test the improvement in the EEG classification performance with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis. The experiment results show that the all subjects achieve a classification accuracy of 100%.
The impact of oil-price shocks on Hawaii's economy: A case study using vector autoregression
International Nuclear Information System (INIS)
Gopalakrishnan, C.; Tian, X.; Tran, D.
1991-01-01
The effects of oil-price shocks on the macroeconomic performance of a non-oil-producing, oil-importing state are studied in terms of Hawaii's experience (1974-1986) using Vector Autoregression (VAR). The VAR model contains three macrovariables-real oil price, interest rate, and real GNP, and three regional variable-total civilian labor force, Honolulu consumer price index, and real personal income. The results suggested that oil-price shock had a positive effect on interest rate as well as local price (i.e., higher interest and higher local price), but a negative influence on real GNP. The negative income effect, however, was offset by the positive employment effect. The price of oil was found to be exogenous to all other variables in the system. The macrovariables exerted a pronounced impact on Hawaii's economy, most notably on consumer price
DEFF Research Database (Denmark)
Lassen Kaspersen, Line; Føyn, Tullik Helene Ystanes
This paper investigates price transmission for agricultural commodities between world markets and the Ugandan market in an attempt to determine the impact of world market prices on the Ugandan market. Based on the realization that price formation is not a static concept, a dynamic vector...... price relations, i.e. the price variations between geographically separated markets in Uganda and the world markets. Our analysis indicates that food markets in Uganda, based on our study of sorghum price transmission, are not integrated into world markets, and that oil prices are a very determining...... autoregressive (VAR) model is presented. The prices of Robusta coffee and sorghum are examined, as both of these crops are important for the domestic economy of Uganda – Robusta as a cash crop, mainly traded internationally, and sorghum for consumption at household level. The analysis focuses on the spatial...
Debt Contagion in Europe: A Panel-Vector Autoregressive (VAR Analysis
Directory of Open Access Journals (Sweden)
Florence Bouvet
2013-12-01
Full Text Available The European sovereign-debt crisis began in Greece when the government announced in December, 2009, that its debt reached 121% of GDP (or 300 billion euros and its 2009 budget deficit was 12.7% of GDP, four times the level allowed by the Maastricht Treaty. The Greek crisis soon spread to other Economic and Monetary Union (EMU countries, notably Ireland, Portugal, Spain and Italy. Using quarterly data for the 2000–2011 period, we implement a panel-vector autoregressive (PVAR model for 11 EMU countries to examine the extent to which a rise in a country’s bond-yield spread or debt-to-GDP ratio affects another EMU countries’ fiscal and macroeconomic outcomes. To distinguish between interdependence and contagion among EMU countries, we compare results obtained for the pre-crisis period (2000–2007 with the crisis period (2008–2011 and control for global risk aversion.
An interpretable LSTM neural network for autoregressive exogenous model
Guo, Tian; Lin, Tao; Lu, Yao
2018-01-01
In this paper, we propose an interpretable LSTM recurrent neural network, i.e., multi-variable LSTM for time series with exogenous variables. Currently, widely used attention mechanism in recurrent neural networks mostly focuses on the temporal aspect of data and falls short of characterizing variable importance. To this end, our multi-variable LSTM equipped with tensorized hidden states is developed to learn variable specific representations, which give rise to both temporal and variable lev...
Bayesian Analysis of Multivariate Threshold Autoregressive Models with Missing Data
Calderón Villanueva, Sergio Alejandro
2014-01-01
Resumen. En algunos campos, nos vemos forzados a trabajar con datos faltantes en series de tiempo multivaridas, desafortunadamente el análisis en este contexto no puede ser hecho como en caso completo. El análisis de modelos multivaridos autoregresivos de umbrales(MTAR) con entradas exógenas y datos faltantes es llevado a cabo vía el enfoque Bayesiano. Los métodos MCMC son usados para obtener muestras de las distribuciones marginales aposteriori, incluyendo los valores de los umbrales y los d...
Identification of Civil Engineering Structures using Multivariate ARMAV and RARMAV Models
DEFF Research Database (Denmark)
Kirkegaard, Poul Henning; Andersen, P.; Brincker, Rune
This paper presents how to make system identification of civil engineering structures using multivariate auto-regressive moving-average vector (ARMAV) models. Further, the ARMAV technique is extended to a recursive technique (RARMAV). The ARMAV model is used to identify measured stationary data....... The results show the usefulness of the approaches for identification of civil engineering structures excited by natural excitation...
Time-Varying Combinations of Bayesian Dynamic Models and Equity Momentum Strategies
N. Basturk (Nalan); S. Grassi (Stefano); L.F. Hoogerheide (Lennart); H.K. van Dijk (Herman)
2016-01-01
markdownabstractA novel dynamic asset-allocation approach is proposed where portfolios as well as portfolio strategies are updated at every decision period based on their past performance. For modeling, a general class of models is specified that combines a dynamic factor and a vector autoregressive
Distributed simulation a model driven engineering approach
Topçu, Okan; Oğuztüzün, Halit; Yilmaz, Levent
2016-01-01
Backed by substantive case studies, the novel approach to software engineering for distributed simulation outlined in this text demonstrates the potent synergies between model-driven techniques, simulation, intelligent agents, and computer systems development.
Service creation: a model-based approach
Quartel, Dick; van Sinderen, Marten J.; Ferreira Pires, Luis
1999-01-01
This paper presents a model-based approach to support service creation. In this approach, services are assumed to be created from (available) software components. The creation process may involve multiple design steps in which the requested service is repeatedly decomposed into more detailed
Models of galaxies - The modal approach
International Nuclear Information System (INIS)
Lin, C.C.; Lowe, S.A.
1990-01-01
The general viability of the modal approach to the spiral structure in normal spirals and the barlike structure in certain barred spirals is discussed. The usefulness of the modal approach in the construction of models of such galaxies is examined, emphasizing the adoption of a model appropriate to observational data for both the spiral structure of a galaxy and its basic mass distribution. 44 refs
Comparison of Classical and Robust Estimates of Threshold Auto-regression Parameters
Directory of Open Access Journals (Sweden)
V. B. Goryainov
2017-01-01
Full Text Available The study object is the first-order threshold auto-regression model with a single zero-located threshold. The model describes a stochastic temporal series with discrete time by means of a piecewise linear equation consisting of two linear classical first-order autoregressive equations. One of these equations is used to calculate a running value of the temporal series. A control variable that determines the choice between these two equations is the sign of the previous value of the same series.The first-order threshold autoregressive model with a single threshold depends on two real parameters that coincide with the coefficients of the piecewise linear threshold equation. These parameters are assumed to be unknown. The paper studies an estimate of the least squares, an estimate the least modules, and the M-estimates of these parameters. The aim of the paper is a comparative study of the accuracy of these estimates for the main probabilistic distributions of the updating process of the threshold autoregressive equation. These probability distributions were normal, contaminated normal, logistic, double-exponential distributions, a Student's distribution with different number of degrees of freedom, and a Cauchy distribution.As a measure of the accuracy of each estimate, was chosen its variance to measure the scattering of the estimate around the estimated parameter. An estimate with smaller variance made from the two estimates was considered to be the best. The variance was estimated by computer simulation. To estimate the smallest modules an iterative weighted least-squares method was used and the M-estimates were done by the method of a deformable polyhedron (the Nelder-Mead method. To calculate the least squares estimate, an explicit analytic expression was used.It turned out that the estimation of least squares is best only with the normal distribution of the updating process. For the logistic distribution and the Student's distribution with the
A Bayesian Approach for Summarizing and Modeling Time-Series Exposure Data with Left Censoring.
Houseman, E Andres; Virji, M Abbas
2017-08-01
were significant in some frequentist models, but in the Bayesian model their credible intervals contained zero; such discrepancies were observed in multiple datasets. Variance components from the Bayesian model reflected substantial autocorrelation, consistent with the frequentist models, except for the auto-regressive moving average model. Plots of means from the Bayesian model showed good fit to the observed data. The proposed Bayesian model provides an approach for modeling non-stationary autocorrelation in a hierarchical modeling framework to estimate task means, standard deviations, quantiles, and parameter estimates for covariates that are less biased and have better performance characteristics than some of the contemporary methods. Published by Oxford University Press on behalf of the British Occupational Hygiene Society 2017.
International Nuclear Information System (INIS)
Iwama, N.; Inoue, A.; Tsukishima, T.; Sato, M.; Kawahata, K.
1981-07-01
A new procedure for the maximum entropy spectral estimation is studied for the purpose of data processing in Fourier transform spectroscopy. The autoregressive model fitting is examined under a least squares criterion based on the Yule-Walker equations. An AIC-like criterion is suggested for selecting the model order. The principal advantage of the new procedure lies in the enhanced frequency resolution particularly for small values of the maximum optical path-difference of the interferogram. The usefulness of the procedure is ascertained by some numerical simulations and further by experiments with respect to a highly coherent submillimeter wave and the electron cyclotron emission from a stellarator plasma. (author)
Multiscale approach to equilibrating model polymer melts
DEFF Research Database (Denmark)
Svaneborg, Carsten; Ali Karimi-Varzaneh, Hossein; Hojdis, Nils
2016-01-01
We present an effective and simple multiscale method for equilibrating Kremer Grest model polymer melts of varying stiffness. In our approach, we progressively equilibrate the melt structure above the tube scale, inside the tube and finally at the monomeric scale. We make use of models designed...
Application of various FLD modelling approaches
Banabic, D.; Aretz, H.; Paraianu, L.; Jurco, P.
2005-07-01
This paper focuses on a comparison between different modelling approaches to predict the forming limit diagram (FLD) for sheet metal forming under a linear strain path using the recently introduced orthotropic yield criterion BBC2003 (Banabic D et al 2005 Int. J. Plasticity 21 493-512). The FLD models considered here are a finite element based approach, the well known Marciniak-Kuczynski model, the modified maximum force criterion according to Hora et al (1996 Proc. Numisheet'96 Conf. (Dearborn/Michigan) pp 252-6), Swift's diffuse (Swift H W 1952 J. Mech. Phys. Solids 1 1-18) and Hill's classical localized necking approach (Hill R 1952 J. Mech. Phys. Solids 1 19-30). The FLD of an AA5182-O aluminium sheet alloy has been determined experimentally in order to quantify the predictive capabilities of the models mentioned above.
Risk Modelling for Passages in Approach Channel
Directory of Open Access Journals (Sweden)
Leszek Smolarek
2013-01-01
Full Text Available Methods of multivariate statistics, stochastic processes, and simulation methods are used to identify and assess the risk measures. This paper presents the use of generalized linear models and Markov models to study risks to ships along the approach channel. These models combined with simulation testing are used to determine the time required for continuous monitoring of endangered objects or period at which the level of risk should be verified.
NONPARAMETRIC IDENTIFICATION FOR NONLINEAR AUTOREGRESSIVE TIMESERIES MODELS： CONVERGENCE RATES
Institute of Scientific and Technical Information of China (English)
LUZUDI; CHENGPING
1999-01-01
In this paper the optimal convergence rates of estimators ba~ed on kernel approach fornonlinear AR model are investigated in the sense of Stone[17'1a]. By combining the mixingproperty of the stationary solution with the characteristics of the model itself, the restrictiveconditions in the literature which are not easy to be satisfied by the nonlinear AR model axeremoved, and the mild conditions are obtained to guarantee the optimal ratea of the estimatorof autoregTession function. In addition: the strongly coasistent estimator of the ~riance ofwhite noise is also constructed.
Time series segmentation: a new approach based on Genetic Algorithm and Hidden Markov Model
Toreti, A.; Kuglitsch, F. G.; Xoplaki, E.; Luterbacher, J.
2009-04-01
The subdivision of a time series into homogeneous segments has been performed using various methods applied to different disciplines. In climatology, for example, it is accompanied by the well-known homogenization problem and the detection of artificial change points. In this context, we present a new method (GAMM) based on Hidden Markov Model (HMM) and Genetic Algorithm (GA), applicable to series of independent observations (and easily adaptable to autoregressive processes). A left-to-right hidden Markov model, estimating the parameters and the best-state sequence, respectively, with the Baum-Welch and Viterbi algorithms, was applied. In order to avoid the well-known dependence of the Baum-Welch algorithm on the initial condition, a Genetic Algorithm was developed. This algorithm is characterized by mutation, elitism and a crossover procedure implemented with some restrictive rules. Moreover the function to be minimized was derived following the approach of Kehagias (2004), i.e. it is the so-called complete log-likelihood. The number of states was determined applying a two-fold cross-validation procedure (Celeux and Durand, 2008). Being aware that the last issue is complex, and it influences all the analysis, a Multi Response Permutation Procedure (MRPP; Mielke et al., 1981) was inserted. It tests the model with K+1 states (where K is the state number of the best model) if its likelihood is close to K-state model. Finally, an evaluation of the GAMM performances, applied as a break detection method in the field of climate time series homogenization, is shown. 1. G. Celeux and J.B. Durand, Comput Stat 2008. 2. A. Kehagias, Stoch Envir Res 2004. 3. P.W. Mielke, K.J. Berry, G.W. Brier, Monthly Wea Rev 1981.
Set-Theoretic Approach to Maturity Models
DEFF Research Database (Denmark)
Lasrado, Lester Allan
Despite being widely accepted and applied, maturity models in Information Systems (IS) have been criticized for the lack of theoretical grounding, methodological rigor, empirical validations, and ignorance of multiple and non-linear paths to maturity. This PhD thesis focuses on addressing...... these criticisms by incorporating recent developments in configuration theory, in particular application of set-theoretic approaches. The aim is to show the potential of employing a set-theoretic approach for maturity model research and empirically demonstrating equifinal paths to maturity. Specifically...... methodological guidelines consisting of detailed procedures to systematically apply set theoretic approaches for maturity model research and provides demonstrations of it application on three datasets. The thesis is a collection of six research papers that are written in a sequential manner. The first paper...
MacNab, Ying C
2016-08-01
This paper concerns with multivariate conditional autoregressive models defined by linear combination of independent or correlated underlying spatial processes. Known as linear models of coregionalization, the method offers a systematic and unified approach for formulating multivariate extensions to a broad range of univariate conditional autoregressive models. The resulting multivariate spatial models represent classes of coregionalized multivariate conditional autoregressive models that enable flexible modelling of multivariate spatial interactions, yielding coregionalization models with symmetric or asymmetric cross-covariances of different spatial variation and smoothness. In the context of multivariate disease mapping, for example, they facilitate borrowing strength both over space and cross variables, allowing for more flexible multivariate spatial smoothing. Specifically, we present a broadened coregionalization framework to include order-dependent, order-free, and order-robust multivariate models; a new class of order-free coregionalized multivariate conditional autoregressives is introduced. We tackle computational challenges and present solutions that are integral for Bayesian analysis of these models. We also discuss two ways of computing deviance information criterion for comparison among competing hierarchical models with or without unidentifiable prior parameters. The models and related methodology are developed in the broad context of modelling multivariate data on spatial lattice and illustrated in the context of multivariate disease mapping. The coregionalization framework and related methods also present a general approach for building spatially structured cross-covariance functions for multivariate geostatistics. © The Author(s) 2016.
Mathematical Modeling Approaches in Plant Metabolomics.
Fürtauer, Lisa; Weiszmann, Jakob; Weckwerth, Wolfram; Nägele, Thomas
2018-01-01
The experimental analysis of a plant metabolome typically results in a comprehensive and multidimensional data set. To interpret metabolomics data in the context of biochemical regulation and environmental fluctuation, various approaches of mathematical modeling have been developed and have proven useful. In this chapter, a general introduction to mathematical modeling is presented and discussed in context of plant metabolism. A particular focus is laid on the suitability of mathematical approaches to functionally integrate plant metabolomics data in a metabolic network and combine it with other biochemical or physiological parameters.
Directory of Open Access Journals (Sweden)
Fabyano Fonseca e Silva
2011-04-01
Full Text Available The animal breeding values forecasting at futures times is a relevant technological innovation in the field of Animal Science, since its enables a previous indication of animals that will be either kept by the producer for breeding purposes or discarded. This study discusses an MCMC Bayesian methodology applied to panel data in a time series context. We consider Bayesian analysis of an autoregressive, AR(p, panel data model of order p, using an exact likelihood function, comparative analysis of prior distributions and predictive distributions of future observations. The methodology was tested by a simulation study using three priors: hierarchical Multivariate Normal-Inverse Gamma (model 1, independent Multivariate Student's t Inverse Gamma (model 2 and Jeffrey's (model 3. Comparisons by Pseudo-Bayes Factor favored model 2. The proposed methodology was applied to longitudinal data relative to Expected Progeny Difference (EPD of beef cattle sires. The forecast efficiency was around 80%. Regarding the mean width of the EPD interval estimation (95% in a future time, a great advantage was observed for the proposed Bayesian methodology over usual asymptotic frequentist method.A previsão dos valores genéticos de animais em tempos futuros constitui importante inovação tecnológica para a área de Zootecnia, uma vez que possibilita planejar com antecedência o descarte ou a manutenção de animais no rebanho. No presente estudo considerou-se uma análise Bayesiana de modelos auto-regressivos de ordem p, AR(p, para dados em painel, de forma a utilizar a função de verossimilhança exata, a análise de comparação de distribuições a priori e a obtenção de distribuições preditivas de dados futuros. A metodologia utilizada foi testada mediante um estudo de simulação usando a priori hierárquica Normal multivariada-Gama inversa (modelo 1, a priori independente t-Student Gama inversa (modelo 2 e a priori de Jeffreys (modelo 3. As compara
SLS Navigation Model-Based Design Approach
Oliver, T. Emerson; Anzalone, Evan; Geohagan, Kevin; Bernard, Bill; Park, Thomas
2018-01-01
The SLS Program chose to implement a Model-based Design and Model-based Requirements approach for managing component design information and system requirements. This approach differs from previous large-scale design efforts at Marshall Space Flight Center where design documentation alone conveyed information required for vehicle design and analysis and where extensive requirements sets were used to scope and constrain the design. The SLS Navigation Team has been responsible for the Program-controlled Design Math Models (DMMs) which describe and represent the performance of the Inertial Navigation System (INS) and the Rate Gyro Assemblies (RGAs) used by Guidance, Navigation, and Controls (GN&C). The SLS Navigation Team is also responsible for the navigation algorithms. The navigation algorithms are delivered for implementation on the flight hardware as a DMM. For the SLS Block 1-B design, the additional GPS Receiver hardware is managed as a DMM at the vehicle design level. This paper provides a discussion of the processes and methods used to engineer, design, and coordinate engineering trades and performance assessments using SLS practices as applied to the GN&C system, with a particular focus on the Navigation components. These include composing system requirements, requirements verification, model development, model verification and validation, and modeling and analysis approaches. The Model-based Design and Requirements approach does not reduce the effort associated with the design process versus previous processes used at Marshall Space Flight Center. Instead, the approach takes advantage of overlap between the requirements development and management process, and the design and analysis process by efficiently combining the control (i.e. the requirement) and the design mechanisms. The design mechanism is the representation of the component behavior and performance in design and analysis tools. The focus in the early design process shifts from the development and
International Nuclear Information System (INIS)
Yu, Jie; Chen, Kuilin; Mori, Junichi; Rashid, Mudassir M.
2013-01-01
Optimizing wind power generation and controlling the operation of wind turbines to efficiently harness the renewable wind energy is a challenging task due to the intermittency and unpredictable nature of wind speed, which has significant influence on wind power production. A new approach for long-term wind speed forecasting is developed in this study by integrating GMCM (Gaussian mixture copula model) and localized GPR (Gaussian process regression). The time series of wind speed is first classified into multiple non-Gaussian components through the Gaussian mixture copula model and then Bayesian inference strategy is employed to incorporate the various non-Gaussian components using the posterior probabilities. Further, the localized Gaussian process regression models corresponding to different non-Gaussian components are built to characterize the stochastic uncertainty and non-stationary seasonality of the wind speed data. The various localized GPR models are integrated through the posterior probabilities as the weightings so that a global predictive model is developed for the prediction of wind speed. The proposed GMCM–GPR approach is demonstrated using wind speed data from various wind farm locations and compared against the GMCM-based ARIMA (auto-regressive integrated moving average) and SVR (support vector regression) methods. In contrast to GMCM–ARIMA and GMCM–SVR methods, the proposed GMCM–GPR model is able to well characterize the multi-seasonality and uncertainty of wind speed series for accurate long-term prediction. - Highlights: • A novel predictive modeling method is proposed for long-term wind speed forecasting. • Gaussian mixture copula model is estimated to characterize the multi-seasonality. • Localized Gaussian process regression models can deal with the random uncertainty. • Multiple GPR models are integrated through Bayesian inference strategy. • The proposed approach shows higher prediction accuracy and reliability
Stochastic approaches to inflation model building
International Nuclear Information System (INIS)
Ramirez, Erandy; Liddle, Andrew R.
2005-01-01
While inflation gives an appealing explanation of observed cosmological data, there are a wide range of different inflation models, providing differing predictions for the initial perturbations. Typically models are motivated either by fundamental physics considerations or by simplicity. An alternative is to generate large numbers of models via a random generation process, such as the flow equations approach. The flow equations approach is known to predict a definite structure to the observational predictions. In this paper, we first demonstrate a more efficient implementation of the flow equations exploiting an analytic solution found by Liddle (2003). We then consider alternative stochastic methods of generating large numbers of inflation models, with the aim of testing whether the structures generated by the flow equations are robust. We find that while typically there remains some concentration of points in the observable plane under the different methods, there is significant variation in the predictions amongst the methods considered
Model validation: a systemic and systematic approach
International Nuclear Information System (INIS)
Sheng, G.; Elzas, M.S.; Cronhjort, B.T.
1993-01-01
The term 'validation' is used ubiquitously in association with the modelling activities of numerous disciplines including social, political natural, physical sciences, and engineering. There is however, a wide range of definitions which give rise to very different interpretations of what activities the process involves. Analyses of results from the present large international effort in modelling radioactive waste disposal systems illustrate the urgent need to develop a common approach to model validation. Some possible explanations are offered to account for the present state of affairs. The methodology developed treats model validation and code verification in a systematic fashion. In fact, this approach may be regarded as a comprehensive framework to assess the adequacy of any simulation study. (author)
A Conceptual Modeling Approach for OLAP Personalization
Garrigós, Irene; Pardillo, Jesús; Mazón, Jose-Norberto; Trujillo, Juan
Data warehouses rely on multidimensional models in order to provide decision makers with appropriate structures to intuitively analyze data with OLAP technologies. However, data warehouses may be potentially large and multidimensional structures become increasingly complex to be understood at a glance. Even if a departmental data warehouse (also known as data mart) is used, these structures would be also too complex. As a consequence, acquiring the required information is more costly than expected and decision makers using OLAP tools may get frustrated. In this context, current approaches for data warehouse design are focused on deriving a unique OLAP schema for all analysts from their previously stated information requirements, which is not enough to lighten the complexity of the decision making process. To overcome this drawback, we argue for personalizing multidimensional models for OLAP technologies according to the continuously changing user characteristics, context, requirements and behaviour. In this paper, we present a novel approach to personalizing OLAP systems at the conceptual level based on the underlying multidimensional model of the data warehouse, a user model and a set of personalization rules. The great advantage of our approach is that a personalized OLAP schema is provided for each decision maker contributing to better satisfy their specific analysis needs. Finally, we show the applicability of our approach through a sample scenario based on our CASE tool for data warehouse development.
Variational approach to chiral quark models
Energy Technology Data Exchange (ETDEWEB)
Futami, Yasuhiko; Odajima, Yasuhiko; Suzuki, Akira
1987-03-01
A variational approach is applied to a chiral quark model to test the validity of the perturbative treatment of the pion-quark interaction based on the chiral symmetry principle. It is indispensably related to the chiral symmetry breaking radius if the pion-quark interaction can be regarded as a perturbation.
A variational approach to chiral quark models
International Nuclear Information System (INIS)
Futami, Yasuhiko; Odajima, Yasuhiko; Suzuki, Akira.
1987-01-01
A variational approach is applied to a chiral quark model to test the validity of the perturbative treatment of the pion-quark interaction based on the chiral symmetry principle. It is indispensably related to the chiral symmetry breaking radius if the pion-quark interaction can be regarded as a perturbation. (author)
A Set Theoretical Approach to Maturity Models
DEFF Research Database (Denmark)
Lasrado, Lester; Vatrapu, Ravi; Andersen, Kim Normann
2016-01-01
characterized by equifinality, multiple conjunctural causation, and case diversity. We prescribe methodological guidelines consisting of a six-step procedure to systematically apply set theoretic methods to conceptualize, develop, and empirically derive maturity models and provide a demonstration......Maturity Model research in IS has been criticized for the lack of theoretical grounding, methodological rigor, empirical validations, and ignorance of multiple and non-linear paths to maturity. To address these criticisms, this paper proposes a novel set-theoretical approach to maturity models...
A Stochastic Approach to Noise Modeling for Barometric Altimeters
Directory of Open Access Journals (Sweden)
Angelo Maria Sabatini
2013-11-01
Full Text Available The question whether barometric altimeters can be applied to accurately track human motions is still debated, since their measurement performance are rather poor due to either coarse resolution or drifting behavior problems. As a step toward accurate short-time tracking of changes in height (up to few minutes, we develop a stochastic model that attempts to capture some statistical properties of the barometric altimeter noise. The barometric altimeter noise is decomposed in three components with different physical origin and properties: a deterministic time-varying mean, mainly correlated with global environment changes, and a first-order Gauss-Markov (GM random process, mainly accounting for short-term, local environment changes, the effects of which are prominent, respectively, for long-time and short-time motion tracking; an uncorrelated random process, mainly due to wideband electronic noise, including quantization noise. Autoregressive-moving average (ARMA system identification techniques are used to capture the correlation structure of the piecewise stationary GM component, and to estimate its standard deviation, together with the standard deviation of the uncorrelated component. M-point moving average filters used alone or in combination with whitening filters learnt from ARMA model parameters are further tested in few dynamic motion experiments and discussed for their capability of short-time tracking small-amplitude, low-frequency motions.
A hybrid modeling approach for option pricing
Hajizadeh, Ehsan; Seifi, Abbas
2011-11-01
The complexity of option pricing has led many researchers to develop sophisticated models for such purposes. The commonly used Black-Scholes model suffers from a number of limitations. One of these limitations is the assumption that the underlying probability distribution is lognormal and this is so controversial. We propose a couple of hybrid models to reduce these limitations and enhance the ability of option pricing. The key input to option pricing model is volatility. In this paper, we use three popular GARCH type model for estimating volatility. Then, we develop two non-parametric models based on neural networks and neuro-fuzzy networks to price call options for S&P 500 index. We compare the results with those of Black-Scholes model and show that both neural network and neuro-fuzzy network models outperform Black-Scholes model. Furthermore, comparing the neural network and neuro-fuzzy approaches, we observe that for at-the-money options, neural network model performs better and for both in-the-money and an out-of-the money option, neuro-fuzzy model provides better results.
Heat transfer modeling an inductive approach
Sidebotham, George
2015-01-01
This innovative text emphasizes a "less-is-more" approach to modeling complicated systems such as heat transfer by treating them first as "1-node lumped models" that yield simple closed-form solutions. The author develops numerical techniques for students to obtain more detail, but also trains them to use the techniques only when simpler approaches fail. Covering all essential methods offered in traditional texts, but with a different order, Professor Sidebotham stresses inductive thinking and problem solving as well as a constructive understanding of modern, computer-based practice. Readers learn to develop their own code in the context of the material, rather than just how to use packaged software, offering a deeper, intrinsic grasp behind models of heat transfer. Developed from over twenty-five years of lecture notes to teach students of mechanical and chemical engineering at The Cooper Union for the Advancement of Science and Art, the book is ideal for students and practitioners across engineering discipl...
Nonperturbative approach to the attractive Hubbard model
International Nuclear Information System (INIS)
Allen, S.; Tremblay, A.-M. S.
2001-01-01
A nonperturbative approach to the single-band attractive Hubbard model is presented in the general context of functional-derivative approaches to many-body theories. As in previous work on the repulsive model, the first step is based on a local-field-type ansatz, on enforcement of the Pauli principle and a number of crucial sumrules. The Mermin-Wagner theorem in two dimensions is automatically satisfied. At this level, two-particle self-consistency has been achieved. In the second step of the approximation, an improved expression for the self-energy is obtained by using the results of the first step in an exact expression for the self-energy, where the high- and low-frequency behaviors appear separately. The result is a cooperon-like formula. The required vertex corrections are included in this self-energy expression, as required by the absence of a Migdal theorem for this problem. Other approaches to the attractive Hubbard model are critically compared. Physical consequences of the present approach and agreement with Monte Carlo simulations are demonstrated in the accompanying paper (following this one)
Quasirelativistic quark model in quasipotential approach
Matveev, V A; Savrin, V I; Sissakian, A N
2002-01-01
The relativistic particles interaction is described within the frames of quasipotential approach. The presentation is based on the so called covariant simultaneous formulation of the quantum field theory, where by the theory is considered on the spatial-like three-dimensional hypersurface in the Minkowski space. Special attention is paid to the methods of plotting various quasipotentials as well as to the applications of the quasipotential approach to describing the characteristics of the relativistic particles interaction in the quark models, namely: the hadrons elastic scattering amplitudes, the mass spectra and widths mesons decays, the cross sections of the deep inelastic leptons scattering on the hadrons
A multiscale modeling approach for biomolecular systems
Energy Technology Data Exchange (ETDEWEB)
Bowling, Alan, E-mail: bowling@uta.edu; Haghshenas-Jaryani, Mahdi, E-mail: mahdi.haghshenasjaryani@mavs.uta.edu [The University of Texas at Arlington, Department of Mechanical and Aerospace Engineering (United States)
2015-04-15
This paper presents a new multiscale molecular dynamic model for investigating the effects of external interactions, such as contact and impact, during stepping and docking of motor proteins and other biomolecular systems. The model retains the mass properties ensuring that the result satisfies Newton’s second law. This idea is presented using a simple particle model to facilitate discussion of the rigid body model; however, the particle model does provide insights into particle dynamics at the nanoscale. The resulting three-dimensional model predicts a significant decrease in the effect of the random forces associated with Brownian motion. This conclusion runs contrary to the widely accepted notion that the motor protein’s movements are primarily the result of thermal effects. This work focuses on the mechanical aspects of protein locomotion; the effect ATP hydrolysis is estimated as internal forces acting on the mechanical model. In addition, the proposed model can be numerically integrated in a reasonable amount of time. Herein, the differences between the motion predicted by the old and new modeling approaches are compared using a simplified model of myosin V.
A new approach for developing adjoint models
Farrell, P. E.; Funke, S. W.
2011-12-01
Many data assimilation algorithms rely on the availability of gradients of misfit functionals, which can be efficiently computed with adjoint models. However, the development of an adjoint model for a complex geophysical code is generally very difficult. Algorithmic differentiation (AD, also called automatic differentiation) offers one strategy for simplifying this task: it takes the abstraction that a model is a sequence of primitive instructions, each of which may be differentiated in turn. While extremely successful, this low-level abstraction runs into time-consuming difficulties when applied to the whole codebase of a model, such as differentiating through linear solves, model I/O, calls to external libraries, language features that are unsupported by the AD tool, and the use of multiple programming languages. While these difficulties can be overcome, it requires a large amount of technical expertise and an intimate familiarity with both the AD tool and the model. An alternative to applying the AD tool to the whole codebase is to assemble the discrete adjoint equations and use these to compute the necessary gradients. With this approach, the AD tool must be applied to the nonlinear assembly operators, which are typically small, self-contained units of the codebase. The disadvantage of this approach is that the assembly of the discrete adjoint equations is still very difficult to perform correctly, especially for complex multiphysics models that perform temporal integration; as it stands, this approach is as difficult and time-consuming as applying AD to the whole model. In this work, we have developed a library which greatly simplifies and automates the alternate approach of assembling the discrete adjoint equations. We propose a complementary, higher-level abstraction to that of AD: that a model is a sequence of linear solves. The developer annotates model source code with library calls that build a 'tape' of the operators involved and their dependencies, and
Eutrophication Modeling Using Variable Chlorophyll Approach
International Nuclear Information System (INIS)
Abdolabadi, H.; Sarang, A.; Ardestani, M.; Mahjoobi, E.
2016-01-01
In this study, eutrophication was investigated in Lake Ontario to identify the interactions among effective drivers. The complexity of such phenomenon was modeled using a system dynamics approach based on a consideration of constant and variable stoichiometric ratios. The system dynamics approach is a powerful tool for developing object-oriented models to simulate complex phenomena that involve feedback effects. Utilizing stoichiometric ratios is a method for converting the concentrations of state variables. During the physical segmentation of the model, Lake Ontario was divided into two layers, i.e., the epilimnion and hypolimnion, and differential equations were developed for each layer. The model structure included 16 state variables related to phytoplankton, herbivorous zooplankton, carnivorous zooplankton, ammonium, nitrate, dissolved phosphorus, and particulate and dissolved carbon in the epilimnion and hypolimnion during a time horizon of one year. The results of several tests to verify the model, close to 1 Nash-Sutcliff coefficient (0.98), the data correlation coefficient (0.98), and lower standard errors (0.96), have indicated well-suited model’s efficiency. The results revealed that there were significant differences in the concentrations of the state variables in constant and variable stoichiometry simulations. Consequently, the consideration of variable stoichiometric ratios in algae and nutrient concentration simulations may be applied in future modeling studies to enhance the accuracy of the results and reduce the likelihood of inefficient control policies.
Sensor network based solar forecasting using a local vector autoregressive ridge framework
Energy Technology Data Exchange (ETDEWEB)
Xu, J. [Stony Brook Univ., NY (United States); Yoo, S. [Brookhaven National Lab. (BNL), Upton, NY (United States); Heiser, J. [Brookhaven National Lab. (BNL), Upton, NY (United States); Kalb, P. [Brookhaven National Lab. (BNL), Upton, NY (United States)
2016-04-04
The significant improvements and falling costs of photovoltaic (PV) technology make solar energy a promising resource, yet the cloud induced variability of surface solar irradiance inhibits its effective use in grid-tied PV generation. Short-term irradiance forecasting, especially on the minute scale, is critically important for grid system stability and auxiliary power source management. Compared to the trending sky imaging devices, irradiance sensors are inexpensive and easy to deploy but related forecasting methods have not been well researched. The prominent challenge of applying classic time series models on a network of irradiance sensors is to address their varying spatio-temporal correlations due to local changes in cloud conditions. We propose a local vector autoregressive framework with ridge regularization to forecast irradiance without explicitly determining the wind field or cloud movement. By using local training data, our learned forecast model is adaptive to local cloud conditions and by using regularization, we overcome the risk of overfitting from the limited training data. Our systematic experimental results showed an average of 19.7% RMSE and 20.2% MAE improvement over the benchmark Persistent Model for 1-5 minute forecasts on a comprehensive 25-day dataset.
Hong, Sehee; Kim, Soyoung
2018-01-01
There are basically two modeling approaches applicable to analyzing an actor-partner interdependence model: the multilevel modeling (hierarchical linear model) and the structural equation modeling. This article explains how to use these two models in analyzing an actor-partner interdependence model and how these two approaches work differently. As an empirical example, marital conflict data were used to analyze an actor-partner interdependence model. The multilevel modeling and the structural equation modeling produced virtually identical estimates for a basic model. However, the structural equation modeling approach allowed more realistic assumptions on measurement errors and factor loadings, rendering better model fit indices.
Evolutionary modeling-based approach for model errors correction
Directory of Open Access Journals (Sweden)
S. Q. Wan
2012-08-01
Full Text Available The inverse problem of using the information of historical data to estimate model errors is one of the science frontier research topics. In this study, we investigate such a problem using the classic Lorenz (1963 equation as a prediction model and the Lorenz equation with a periodic evolutionary function as an accurate representation of reality to generate "observational data."
On the basis of the intelligent features of evolutionary modeling (EM, including self-organization, self-adaptive and self-learning, the dynamic information contained in the historical data can be identified and extracted by computer automatically. Thereby, a new approach is proposed to estimate model errors based on EM in the present paper. Numerical tests demonstrate the ability of the new approach to correct model structural errors. In fact, it can actualize the combination of the statistics and dynamics to certain extent.
A multilevel nonlinear mixed-effects approach to model growth in pigs
DEFF Research Database (Denmark)
Strathe, Anders Bjerring; Danfær, Allan Christian; Sørensen, H.
2010-01-01
Growth functions have been used to predict market weight of pigs and maximize return over feed costs. This study was undertaken to compare 4 growth functions and methods of analyzing data, particularly one that considers nonlinear repeated measures. Data were collected from an experiment with 40...... pigs maintained from birth to maturity and their BW measured weekly or every 2 wk up to 1,007 d. Gompertz, logistic, Bridges, and Lopez functions were fitted to the data and compared using information criteria. For each function, a multilevel nonlinear mixed effects model was employed because....... Furthermore, studies should consider adding continuous autoregressive process when analyzing nonlinear mixed models with repeated measures....
MODELS OF TECHNOLOGY ADOPTION: AN INTEGRATIVE APPROACH
Directory of Open Access Journals (Sweden)
Andrei OGREZEANU
2015-06-01
Full Text Available The interdisciplinary study of information technology adoption has developed rapidly over the last 30 years. Various theoretical models have been developed and applied such as: the Technology Acceptance Model (TAM, Innovation Diffusion Theory (IDT, Theory of Planned Behavior (TPB, etc. The result of these many years of research is thousands of contributions to the field, which, however, remain highly fragmented. This paper develops a theoretical model of technology adoption by integrating major theories in the field: primarily IDT, TAM, and TPB. To do so while avoiding mess, an approach that goes back to basics in independent variable type’s development is proposed; emphasizing: 1 the logic of classification, and 2 psychological mechanisms behind variable types. Once developed these types are then populated with variables originating in empirical research. Conclusions are developed on which types are underpopulated and present potential for future research. I end with a set of methodological recommendations for future application of the model.
Interfacial Fluid Mechanics A Mathematical Modeling Approach
Ajaev, Vladimir S
2012-01-01
Interfacial Fluid Mechanics: A Mathematical Modeling Approach provides an introduction to mathematical models of viscous flow used in rapidly developing fields of microfluidics and microscale heat transfer. The basic physical effects are first introduced in the context of simple configurations and their relative importance in typical microscale applications is discussed. Then,several configurations of importance to microfluidics, most notably thin films/droplets on substrates and confined bubbles, are discussed in detail. Topics from current research on electrokinetic phenomena, liquid flow near structured solid surfaces, evaporation/condensation, and surfactant phenomena are discussed in the later chapters. This book also: Discusses mathematical models in the context of actual applications such as electrowetting Includes unique material on fluid flow near structured surfaces and phase change phenomena Shows readers how to solve modeling problems related to microscale multiphase flows Interfacial Fluid Me...
A new modelling approach for zooplankton behaviour
Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.
We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.
Continuum modeling an approach through practical examples
Muntean, Adrian
2015-01-01
This book develops continuum modeling skills and approaches the topic from three sides: (1) derivation of global integral laws together with the associated local differential equations, (2) design of constitutive laws and (3) modeling boundary processes. The focus of this presentation lies on many practical examples covering aspects such as coupled flow, diffusion and reaction in porous media or microwave heating of a pizza, as well as traffic issues in bacterial colonies and energy harvesting from geothermal wells. The target audience comprises primarily graduate students in pure and applied mathematics as well as working practitioners in engineering who are faced by nonstandard rheological topics like those typically arising in the food industry.
Global Environmental Change: An integrated modelling approach
International Nuclear Information System (INIS)
Den Elzen, M.
1993-01-01
Two major global environmental problems are dealt with: climate change and stratospheric ozone depletion (and their mutual interactions), briefly surveyed in part 1. In Part 2 a brief description of the integrated modelling framework IMAGE 1.6 is given. Some specific parts of the model are described in more detail in other Chapters, e.g. the carbon cycle model, the atmospheric chemistry model, the halocarbon model, and the UV-B impact model. In Part 3 an uncertainty analysis of climate change and stratospheric ozone depletion is presented (Chapter 4). Chapter 5 briefly reviews the social and economic uncertainties implied by future greenhouse gas emissions. Chapters 6 and 7 describe a model and sensitivity analysis pertaining to the scientific uncertainties and/or lacunae in the sources and sinks of methane and carbon dioxide, and their biogeochemical feedback processes. Chapter 8 presents an uncertainty and sensitivity analysis of the carbon cycle model, the halocarbon model, and the IMAGE model 1.6 as a whole. Part 4 presents the risk assessment methodology as applied to the problems of climate change and stratospheric ozone depletion more specifically. In Chapter 10, this methodology is used as a means with which to asses current ozone policy and a wide range of halocarbon policies. Chapter 11 presents and evaluates the simulated globally-averaged temperature and sea level rise (indicators) for the IPCC-1990 and 1992 scenarios, concluding with a Low Risk scenario, which would meet the climate targets. Chapter 12 discusses the impact of sea level rise on the frequency of the Dutch coastal defence system (indicator) for the IPCC-1990 scenarios. Chapter 13 presents projections of mortality rates due to stratospheric ozone depletion based on model simulations employing the UV-B chain model for a number of halocarbon policies. Chapter 14 presents an approach for allocating future emissions of CO 2 among regions. (Abstract Truncated)
Porta, Alberto; Bari, Vlasta; Ranuzzi, Giovanni; De Maria, Beatrice; Baselli, Giuseppe
2017-09-01
We propose a multiscale complexity (MSC) method assessing irregularity in assigned frequency bands and being appropriate for analyzing the short time series. It is grounded on the identification of the coefficients of an autoregressive model, on the computation of the mean position of the poles generating the components of the power spectral density in an assigned frequency band, and on the assessment of its distance from the unit circle in the complex plane. The MSC method was tested on simulations and applied to the short heart period (HP) variability series recorded during graded head-up tilt in 17 subjects (age from 21 to 54 years, median = 28 years, 7 females) and during paced breathing protocols in 19 subjects (age from 27 to 35 years, median = 31 years, 11 females) to assess the contribution of time scales typical of the cardiac autonomic control, namely in low frequency (LF, from 0.04 to 0.15 Hz) and high frequency (HF, from 0.15 to 0.5 Hz) bands to the complexity of the cardiac regulation. The proposed MSC technique was compared to a traditional model-free multiscale method grounded on information theory, i.e., multiscale entropy (MSE). The approach suggests that the reduction of HP variability complexity observed during graded head-up tilt is due to a regularization of the HP fluctuations in LF band via a possible intervention of sympathetic control and the decrement of HP variability complexity observed during slow breathing is the result of the regularization of the HP variations in both LF and HF bands, thus implying the action of physiological mechanisms working at time scales even different from that of respiration. MSE did not distinguish experimental conditions at time scales larger than 1. Over a short time series MSC allows a more insightful association between cardiac control complexity and physiological mechanisms modulating cardiac rhythm compared to a more traditional tool such as MSE.
Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian
2014-01-01
Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.
Fong Chao, B.
1983-12-01
The homogeneous set of 80-year-long (1900-1979) International Latitude Service (ILS) polar motion data is analyzed using the autoregressive method (Chao and Gilbert, 1980) which resolves and produces estimates for the complex frequency (or frequency and Q) and complex amplitude (or amplitude and phase) of each harmonic component in the data. Principal conclusion of this analysis are that (1) the ILS data support the multiple-component hypothesis of the Chandler wobble (it is found that the Chandler wobble can be adequately modeled as a linear combination of four (coherent) harmonic components, each of which represents a steady, nearly circular, prograte motion, a behavior that is inconsistent with the hypothesis of a single Chandler period excited in a temporally and/or spatially random fashion). (2) the four-component Chandler wobble model ``explains'' the apparent phase reversal during 1920-1940 and the pre-1950 empirical period-amplitude relation, (3) the annual wobble is shown to be rather stationary over the years both in amplitude and in phase and no evidence is found to support the large variations reported by earlier investigations. (4) the Markowitz wobble is found to support the large variations reported by earlier investigations. (4) the Markowitz wobble is found to be marginally retrograde and appears to have a complicated behavior which cannot be resolved because of the shortness of the data set.
Levine, Matthew E; Albers, David J; Hripcsak, George
2016-01-01
Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.
Merging Digital Surface Models Implementing Bayesian Approaches
Sadeq, H.; Drummond, J.; Li, Z.
2016-06-01
In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades). It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
MERGING DIGITAL SURFACE MODELS IMPLEMENTING BAYESIAN APPROACHES
Directory of Open Access Journals (Sweden)
H. Sadeq
2016-06-01
Full Text Available In this research different DSMs from different sources have been merged. The merging is based on a probabilistic model using a Bayesian Approach. The implemented data have been sourced from very high resolution satellite imagery sensors (e.g. WorldView-1 and Pleiades. It is deemed preferable to use a Bayesian Approach when the data obtained from the sensors are limited and it is difficult to obtain many measurements or it would be very costly, thus the problem of the lack of data can be solved by introducing a priori estimations of data. To infer the prior data, it is assumed that the roofs of the buildings are specified as smooth, and for that purpose local entropy has been implemented. In addition to the a priori estimations, GNSS RTK measurements have been collected in the field which are used as check points to assess the quality of the DSMs and to validate the merging result. The model has been applied in the West-End of Glasgow containing different kinds of buildings, such as flat roofed and hipped roofed buildings. Both quantitative and qualitative methods have been employed to validate the merged DSM. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the well established Maximum Likelihood model and showed similar quantitative statistical results and better qualitative results. Although the proposed model has been applied on DSMs that were derived from satellite imagery, it can be applied to any other sourced DSMs.
A nationwide modelling approach to decommissioning - 16182
International Nuclear Information System (INIS)
Kelly, Bernard; Lowe, Andy; Mort, Paul
2009-01-01
In this paper we describe a proposed UK national approach to modelling decommissioning. For the first time, we shall have an insight into optimizing the safety and efficiency of a national decommissioning strategy. To do this we use the General Case Integrated Waste Algorithm (GIA), a universal model of decommissioning nuclear plant, power plant, waste arisings and the associated knowledge capture. The model scales from individual items of plant through cells, groups of cells, buildings, whole sites and then on up to a national scale. We describe the national vision for GIA which can be broken down into three levels: 1) the capture of the chronological order of activities that an experienced decommissioner would use to decommission any nuclear facility anywhere in the world - this is Level 1 of GIA; 2) the construction of an Operational Research (OR) model based on Level 1 to allow rapid what if scenarios to be tested quickly (Level 2); 3) the construction of a state of the art knowledge capture capability that allows future generations to learn from our current decommissioning experience (Level 3). We show the progress to date in developing GIA in levels 1 and 2. As part of level 1, GIA has assisted in the development of an IMechE professional decommissioning qualification. Furthermore, we describe GIA as the basis of a UK-Owned database of decommissioning norms for such things as costs, productivity, durations etc. From level 2, we report on a pilot study that has successfully tested the basic principles for the OR numerical simulation of the algorithm. We then highlight the advantages of applying the OR modelling approach nationally. In essence, a series of 'what if...' scenarios can be tested that will improve the safety and efficiency of decommissioning. (authors)
Modeling in transport phenomena a conceptual approach
Tosun, Ismail
2007-01-01
Modeling in Transport Phenomena, Second Edition presents and clearly explains with example problems the basic concepts and their applications to fluid flow, heat transfer, mass transfer, chemical reaction engineering and thermodynamics. A balanced approach is presented between analysis and synthesis, students will understand how to use the solution in engineering analysis. Systematic derivations of the equations and the physical significance of each term are given in detail, for students to easily understand and follow up the material. There is a strong incentive in science and engineering to
Nuclear physics for applications. A model approach
International Nuclear Information System (INIS)
Prussin, S.G.
2007-01-01
Written by a researcher and teacher with experience at top institutes in the US and Europe, this textbook provides advanced undergraduates minoring in physics with working knowledge of the principles of nuclear physics. Simplifying models and approaches reveal the essence of the principles involved, with the mathematical and quantum mechanical background integrated in the text where it is needed and not relegated to the appendices. The practicality of the book is enhanced by numerous end-of-chapter problems and solutions available on the Wiley homepage. (orig.)
Pedagogic process modeling: Humanistic-integrative approach
Directory of Open Access Journals (Sweden)
Boritko Nikolaj M.
2007-01-01
Full Text Available The paper deals with some current problems of modeling the dynamics of the subject-features development of the individual. The term "process" is considered in the context of the humanistic-integrative approach, in which the principles of self education are regarded as criteria for efficient pedagogic activity. Four basic characteristics of the pedagogic process are pointed out: intentionality reflects logicality and regularity of the development of the process; discreteness (stageability in dicates qualitative stages through which the pedagogic phenomenon passes; nonlinearity explains the crisis character of pedagogic processes and reveals inner factors of self-development; situationality requires a selection of pedagogic conditions in accordance with the inner factors, which would enable steering the pedagogic process. Offered are two steps for singling out a particular stage and the algorithm for developing an integrative model for it. The suggested conclusions might be of use for further theoretic research, analyses of educational practices and for realistic predicting of pedagogical phenomena. .
A novel approach to pipeline tensioner modeling
Energy Technology Data Exchange (ETDEWEB)
O' Grady, Robert; Ilie, Daniel; Lane, Michael [MCS Software Division, Galway (Ireland)
2009-07-01
As subsea pipeline developments continue to move into deep and ultra-deep water locations, there is an increasing need for the accurate prediction of expected pipeline fatigue life. A significant factor that must be considered as part of this process is the fatigue damage sustained by the pipeline during installation. The magnitude of this installation-related damage is governed by a number of different agents, one of which is the dynamic behavior of the tensioner systems during pipe-laying operations. There are a variety of traditional finite element methods for representing dynamic tensioner behavior. These existing methods, while basic in nature, have been proven to provide adequate forecasts in terms of the dynamic variation in typical installation parameters such as top tension and sagbend/overbend strain. However due to the simplicity of these current approaches, some of them tend to over-estimate the frequency of tensioner pay out/in under dynamic loading. This excessive level of pay out/in motion results in the prediction of additional stress cycles at certain roller beds, which in turn leads to the prediction of unrealistic fatigue damage to the pipeline. This unwarranted fatigue damage then equates to an over-conservative value for the accumulated damage experienced by a pipeline weld during installation, and so leads to a reduction in the estimated fatigue life for the pipeline. This paper describes a novel approach to tensioner modeling which allows for greater control over the velocity of dynamic tensioner pay out/in and so provides a more accurate estimation of fatigue damage experienced by the pipeline during installation. The paper reports on a case study, as outlined in the proceeding section, in which a comparison is made between results from this new tensioner model and from a more conventional approach. The comparison considers typical installation parameters as well as an in-depth look at the predicted fatigue damage for the two methods
Approaches and models of intercultural education
Directory of Open Access Journals (Sweden)
Iván Manuel Sánchez Fontalvo
2013-10-01
Full Text Available Needed to be aware of the need to build an intercultural society, awareness must be assumed in all social spheres, where stands the role play education. A role of transcendental, since it must promote educational spaces to form people with virtues and powers that allow them to live together / as in multicultural contexts and social diversities (sometimes uneven in an increasingly globalized and interconnected world, and foster the development of feelings of civic belonging shared before the neighborhood, city, region and country, allowing them concern and critical judgement to marginalization, poverty, misery and inequitable distribution of wealth, causes of structural violence, but at the same time, wanting to work for the welfare and transformation of these scenarios. Since these budgets, it is important to know the approaches and models of intercultural education that have been developed so far, analysing their impact on the contexts educational where apply.
Transport modeling: An artificial immune system approach
Directory of Open Access Journals (Sweden)
Teodorović Dušan
2006-01-01
Full Text Available This paper describes an artificial immune system approach (AIS to modeling time-dependent (dynamic, real time transportation phenomenon characterized by uncertainty. The basic idea behind this research is to develop the Artificial Immune System, which generates a set of antibodies (decisions, control actions that altogether can successfully cover a wide range of potential situations. The proposed artificial immune system develops antibodies (the best control strategies for different antigens (different traffic "scenarios". This task is performed using some of the optimization or heuristics techniques. Then a set of antibodies is combined to create Artificial Immune System. The developed Artificial Immune transportation systems are able to generalize, adapt, and learn based on new knowledge and new information. Applications of the systems are considered for airline yield management, the stochastic vehicle routing, and real-time traffic control at the isolated intersection. The preliminary research results are very promising.
System approach to modeling of industrial technologies
Toropov, V. S.; Toropov, E. S.
2018-03-01
The authors presented a system of methods for modeling and improving industrial technologies. The system consists of information and software. The information part is structured information about industrial technologies. The structure has its template. The template has several essential categories used to improve the technological process and eliminate weaknesses in the process chain. The base category is the physical effect that takes place when the technical process proceeds. The programming part of the system can apply various methods of creative search to the content stored in the information part of the system. These methods pay particular attention to energy transformations in the technological process. The system application will allow us to systematize the approach to improving technologies and obtaining new technical solutions.
Klos, Anna; Pottiaux, Eric; Van Malderen, Roeland; Bock, Olivier; Bogusz, Janusz
2017-04-01
A synthetic benchmark dataset of Integrated Water Vapour (IWV) was created within the activity of "Data homogenisation" of sub-working group WG3 of COST ES1206 Action. The benchmark dataset was created basing on the analysis of IWV differences retrieved by Global Positioning System (GPS) International GNSS Service (IGS) stations using European Centre for Medium-Range Weather Forecats (ECMWF) reanalysis data (ERA-Interim). Having analysed a set of 120 series of IWV differences (ERAI-GPS) derived for IGS stations, we delivered parameters of a number of gaps and breaks for every certain station. Moreover, we estimated values of trends, significant seasonalities and character of residuals when deterministic model was removed. We tested five different noise models and found that a combination of white and autoregressive processes of first order describes the stochastic part with a good accuracy. Basing on this analysis, we performed Monte Carlo simulations of 25 years long data with two different types of noise: white as well as combination of white and autoregressive processes. We also added few strictly defined offsets, creating three variants of synthetic dataset: easy, less-complicated and fully-complicated. The 'Easy' dataset included seasonal signals (annual, semi-annual, 3 and 4 months if present for a particular station), offsets and white noise. The 'Less-complicated' dataset included above-mentioned, as well as the combination of white and first order autoregressive processes (AR(1)+WH). The 'Fully-complicated' dataset included, beyond above, a trend and gaps. In this research, we show the impact of manual homogenisation on the estimates of trend and its error. We also cross-compare the results for three above-mentioned datasets, as the synthetized noise type might have a significant influence on manual homogenisation. Therefore, it might mostly affect the values of trend and their uncertainties when inappropriately handled. In a future, the synthetic dataset
Modeling water quality in an urban river using hydrological factors--data driven approaches.
Chang, Fi-John; Tsai, Yu-Hsuan; Chen, Pin-An; Coynel, Alexandra; Vachaud, Georges
2015-03-15
Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be
ECOMOD - An ecological approach to radioecological modelling
International Nuclear Information System (INIS)
Sazykina, Tatiana G.
2000-01-01
A unified methodology is proposed to simulate the dynamic processes of radionuclide migration in aquatic food chains in parallel with their stable analogue elements. The distinguishing feature of the unified radioecological/ecological approach is the description of radionuclide migration along with dynamic equations for the ecosystem. The ability of the methodology to predict the results of radioecological experiments is demonstrated by an example of radionuclide (iron group) accumulation by a laboratory culture of the algae Platymonas viridis. Based on the unified methodology, the 'ECOMOD' radioecological model was developed to simulate dynamic radioecological processes in aquatic ecosystems. It comprises three basic modules, which are operated as a set of inter-related programs. The 'ECOSYSTEM' module solves non-linear ecological equations, describing the biomass dynamics of essential ecosystem components. The 'RADIONUCLIDE DISTRIBUTION' module calculates the radionuclide distribution in abiotic and biotic components of the aquatic ecosystem. The 'DOSE ASSESSMENT' module calculates doses to aquatic biota and doses to man from aquatic food chains. The application of the ECOMOD model to reconstruct the radionuclide distribution in the Chernobyl Cooling Pond ecosystem in the early period after the accident shows good agreement with observations
Modelling Approach In Islamic Architectural Designs
Directory of Open Access Journals (Sweden)
Suhaimi Salleh
2014-06-01
Full Text Available Architectural designs contribute as one of the main factors that should be considered in minimizing negative impacts in planning and structural development in buildings such as in mosques. In this paper, the ergonomics perspective is revisited which hence focuses on the conditional factors involving organisational, psychological, social and population as a whole. This paper tries to highlight the functional and architectural integration with ecstatic elements in the form of decorative and ornamental outlay as well as incorporating the building structure such as wall, domes and gates. This paper further focuses the mathematical aspects of the architectural designs such as polar equations and the golden ratio. These designs are modelled into mathematical equations of various forms, while the golden ratio in mosque is verified using two techniques namely, the geometric construction and the numerical method. The exemplary designs are taken from theSabah Bandaraya Mosque in Likas, Kota Kinabalu and the Sarawak State Mosque in Kuching,while the Universiti Malaysia Sabah Mosque is used for the Golden Ratio. Results show thatIslamic architectural buildings and designs have long had mathematical concepts and techniques underlying its foundation, hence, a modelling approach is needed to rejuvenate these Islamic designs.
Stochastic Modelling of Shiroro River Stream flow Process
Musa, J. J
2013-01-01
Economists, social scientists and engineers provide insights into the drivers of anthropogenic climate change and the options for adaptation and mitigation, and yet other scientists, including geographers and biologists, study the impacts of climate change. This project concentrates mainly on the discharge from the Shiroro River. A stochastic approach is presented for modeling a time series by an Autoregressive Moving Average model (ARMA). The development and use of a stochastic stream flow m...
Porto, Markus; Roman, H Eduardo
2002-04-01
We consider autoregressive conditional heteroskedasticity (ARCH) processes in which the variance sigma(2)(y) depends linearly on the absolute value of the random variable y as sigma(2)(y) = a+b absolute value of y. While for the standard model, where sigma(2)(y) = a + b y(2), the corresponding probability distribution function (PDF) P(y) decays as a power law for absolute value of y-->infinity, in the linear case it decays exponentially as P(y) approximately exp(-alpha absolute value of y), with alpha = 2/b. We extend these results to the more general case sigma(2)(y) = a+b absolute value of y(q), with 0 history of the ARCH process is taken into account, the resulting PDF becomes a stretched exponential even for q = 1, with a stretched exponent beta = 2/3, in a much better agreement with the empirical data.
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Guiseppe; Rahbæk, Anders; Taylor, A.M. Robert
Many key macro-economic and financial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...
Testing for Co-integration in Vector Autoregressions with Non-Stationary Volatility
DEFF Research Database (Denmark)
Cavaliere, Giuseppe; Rahbek, Anders Christian; Taylor, A. M. Robert
Many key macro-economic and …nancial variables are characterised by permanent changes in unconditional volatility. In this paper we analyse vector autoregressions with non-stationary (unconditional) volatility of a very general form, which includes single and multiple volatility breaks as special...
On the Oracle Property of the Adaptive LASSO in Stationary and Nonstationary Autoregressions
DEFF Research Database (Denmark)
Kock, Anders Bredahl
We show that the Adaptive LASSO is oracle efficient in stationary and non-stationary autoregressions. This means that it estimates parameters consistently, selects the correct sparsity pattern, and estimates the coefficients belonging to the relevant variables at the same asymptotic efficiency...
Short-Term Wave Forecasting with AR models in Real-Time Optimal Control of Wave Energy Converters
Fusco, Francesco; Ringwood, John
2010-01-01
Time domain control of wave energy converters requires knowledge of future incident wave elevation in order to approach conditions for optimal energy extraction. Autoregressive models revealed to be a promising approach to the prediction of future values of the wave elevation only from its past history. Results on real wave observations from different ocean locations show that AR models allow to achieve very good predictions for more than one wave period in the future if ...
An integrated approach to permeability modeling using micro-models
Energy Technology Data Exchange (ETDEWEB)
Hosseini, A.H.; Leuangthong, O.; Deutsch, C.V. [Society of Petroleum Engineers, Canadian Section, Calgary, AB (Canada)]|[Alberta Univ., Edmonton, AB (Canada)
2008-10-15
An important factor in predicting the performance of steam assisted gravity drainage (SAGD) well pairs is the spatial distribution of permeability. Complications that make the inference of a reliable porosity-permeability relationship impossible include the presence of short-scale variability in sand/shale sequences; preferential sampling of core data; and uncertainty in upscaling parameters. Micro-modelling is a simple and effective method for overcoming these complications. This paper proposed a micro-modeling approach to account for sampling bias, small laminated features with high permeability contrast, and uncertainty in upscaling parameters. The paper described the steps and challenges of micro-modeling and discussed the construction of binary mixture geo-blocks; flow simulation and upscaling; extended power law formalism (EPLF); and the application of micro-modeling and EPLF. An extended power-law formalism to account for changes in clean sand permeability as a function of macroscopic shale content was also proposed and tested against flow simulation results. There was close agreement between the model and simulation results. The proposed methodology was also applied to build the porosity-permeability relationship for laminated and brecciated facies of McMurray oil sands. Experimental data was in good agreement with the experimental data. 8 refs., 17 figs.
Risk communication: a mental models approach
National Research Council Canada - National Science Library
Morgan, M. Granger (Millett Granger)
2002-01-01
... information about risks. The procedure uses approaches from risk and decision analysis to identify the most relevant information; it also uses approaches from psychology and communication theory to ensure that its message is understood. This book is written in nontechnical terms, designed to make the approach feasible for anyone willing to try it. It is illustrat...
A Multi-Model Approach for System Diagnosis
DEFF Research Database (Denmark)
Niemann, Hans Henrik; Poulsen, Niels Kjølstad; Bækgaard, Mikkel Ask Buur
2007-01-01
A multi-model approach for system diagnosis is presented in this paper. The relation with fault diagnosis as well as performance validation is considered. The approach is based on testing a number of pre-described models and find which one is the best. It is based on an active approach......,i.e. an auxiliary input to the system is applied. The multi-model approach is applied on a wind turbine system....
Langley, Tessa E; McNeill, Ann; Lewis, Sarah; Szatkowski, Lisa; Quinn, Casey
2012-11-01
To evaluate the effect of tobacco control media campaigns and pharmaceutical company-funded advertising for nicotine replacement therapy (NRT) on smoking cessation activity. Multiple time series analysis using structural vector autoregression, January 2002-May 2010. England and Wales. Tobacco control campaign data from the Central Office of Information; commercial NRT campaign data; data on calls to the National Health Service (NHS) stop smoking helpline from the Department of Health; point-of-sale data on over-the-counter (OTC) sales of NRT; and prescribing data from The Health Improvement Network (THIN), a database of UK primary care records. Monthly calls to the NHS stop smoking helpline and monthly rates of OTC sales and prescribing of NRT. A 1% increase in tobacco control television ratings (TVRs), a standard measure of advertising exposure, was associated with a statistically significant 0.085% increase in calls in the same month (P = 0.007), and no statistically significant effect in subsequent months. Tobacco control TVRs were not associated with OTC NRT sales or prescribed NRT. NRT advertising TVRs had a significant effect on NRT sales which became non-significant in the seasonally adjusted model, and no significant effect on prescribing or calls. Tobacco control campaigns appear to be more effective at triggering quitting behaviour than pharmaceutical company NRT campaigns. Any effect of such campaigns on quitting behaviour seems to be restricted to the month of the campaign, suggesting that such campaigns need to be sustained over time. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.
Modeling of Volatility with Non-linear Time Series Model
Kim Song Yon; Kim Mun Chol
2013-01-01
In this paper, non-linear time series models are used to describe volatility in financial time series data. To describe volatility, two of the non-linear time series are combined into form TAR (Threshold Auto-Regressive Model) with AARCH (Asymmetric Auto-Regressive Conditional Heteroskedasticity) error term and its parameter estimation is studied.
Computational and Game-Theoretic Approaches for Modeling Bounded Rationality
L. Waltman (Ludo)
2011-01-01
textabstractThis thesis studies various computational and game-theoretic approaches to economic modeling. Unlike traditional approaches to economic modeling, the approaches studied in this thesis do not rely on the assumption that economic agents behave in a fully rational way. Instead, economic
Forecasting Hong Kong economy using factor augmented vector autoregression
Pang, Iris Ai Jao
2010-01-01
This work applies the FAVAR model to forecast GDP growth rate, unemployment rate and inflation rate of the Hong Kong economy. There is no factor model forecasting literature on the Hong Kong economy. The objective is to find out whether factor forecasting of using a large dataset can improve forecast performance of the Hong Kong economy. To avoid misspecification of the number of factors in the FAVAR, combination forecasts are constructed. It is found that forecasts from FAVAR model overall o...
A Discrete Monetary Economic Growth Model with the MIU Approach
Directory of Open Access Journals (Sweden)
Wei-Bin Zhang
2008-01-01
Full Text Available This paper proposes an alternative approach to economic growth with money. The production side is the same as the Solow model, the Ramsey model, and the Tobin model. But we deal with behavior of consumers differently from the traditional approaches. The model is influenced by the money-in-the-utility (MIU approach in monetary economics. It provides a mechanism of endogenous saving which the Solow model lacks and avoids the assumption of adding up utility over a period of time upon which the Ramsey approach is based.
Mathematical Modelling Approach in Mathematics Education
Arseven, Ayla
2015-01-01
The topic of models and modeling has come to be important for science and mathematics education in recent years. The topic of "Modeling" topic is especially important for examinations such as PISA which is conducted at an international level and measures a student's success in mathematics. Mathematical modeling can be defined as using…
A Multivariate Approach to Functional Neuro Modeling
DEFF Research Database (Denmark)
Mørch, Niels J.S.
1998-01-01
by the application of linear and more flexible, nonlinear microscopic regression models to a real-world dataset. The dependency of model performance, as quantified by generalization error, on model flexibility and training set size is demonstrated, leading to the important realization that no uniformly optimal model......, provides the basis for a generalization theoretical framework relating model performance to model complexity and dataset size. Briefly summarized the major topics discussed in the thesis include: - An introduction of the representation of functional datasets by pairs of neuronal activity patterns...... exists. - Model visualization and interpretation techniques. The simplicity of this task for linear models contrasts the difficulties involved when dealing with nonlinear models. Finally, a visualization technique for nonlinear models is proposed. A single observation emerges from the thesis...
Rival approaches to mathematical modelling in immunology
Andrew, Sarah M.; Baker, Christopher T. H.; Bocharov, Gennady A.
2007-08-01
In order to formulate quantitatively correct mathematical models of the immune system, one requires an understanding of immune processes and familiarity with a range of mathematical techniques. Selection of an appropriate model requires a number of decisions to be made, including a choice of the modelling objectives, strategies and techniques and the types of model considered as candidate models. The authors adopt a multidisciplinary perspective.
A hybrid agent-based approach for modeling microbiological systems.
Guo, Zaiyi; Sloot, Peter M A; Tay, Joc Cing
2008-11-21
Models for systems biology commonly adopt Differential Equations or Agent-Based modeling approaches for simulating the processes as a whole. Models based on differential equations presuppose phenomenological intracellular behavioral mechanisms, while models based on Multi-Agent approach often use directly translated, and quantitatively less precise if-then logical rule constructs. We propose an extendible systems model based on a hybrid agent-based approach where biological cells are modeled as individuals (agents) while molecules are represented by quantities. This hybridization in entity representation entails a combined modeling strategy with agent-based behavioral rules and differential equations, thereby balancing the requirements of extendible model granularity with computational tractability. We demonstrate the efficacy of this approach with models of chemotaxis involving an assay of 10(3) cells and 1.2x10(6) molecules. The model produces cell migration patterns that are comparable to laboratory observations.
Numerical modelling approach for mine backfill
Indian Academy of Sciences (India)
Muhammad Zaka Emad
2017-07-24
Jul 24, 2017 ... conditions. This paper discusses a numerical modelling strategy for modelling mine backfill material. The .... placed in an ore pass that leads the ore to the ore bin and crusher, from ... 1 year, depending on the mine plan.
Uncertainty in biology a computational modeling approach
Gomez-Cabrero, David
2016-01-01
Computational modeling of biomedical processes is gaining more and more weight in the current research into the etiology of biomedical problems and potential treatment strategies. Computational modeling allows to reduce, refine and replace animal experimentation as well as to translate findings obtained in these experiments to the human background. However these biomedical problems are inherently complex with a myriad of influencing factors, which strongly complicates the model building and validation process. This book wants to address four main issues related to the building and validation of computational models of biomedical processes: Modeling establishment under uncertainty Model selection and parameter fitting Sensitivity analysis and model adaptation Model predictions under uncertainty In each of the abovementioned areas, the book discusses a number of key-techniques by means of a general theoretical description followed by one or more practical examples. This book is intended for graduate stude...
OILMAP: A global approach to spill modeling
International Nuclear Information System (INIS)
Spaulding, M.L.; Howlett, E.; Anderson, E.; Jayko, K.
1992-01-01
OILMAP is an oil spill model system suitable for use in both rapid response mode and long-range contingency planning. It was developed for a personal computer and employs full-color graphics to enter data, set up spill scenarios, and view model predictions. The major components of OILMAP include environmental data entry and viewing capabilities, the oil spill models, and model prediction display capabilities. Graphic routines are provided for entering wind data, currents, and any type of geographically referenced data. Several modes of the spill model are available. The surface trajectory mode is intended for quick spill response. The weathering model includes the spreading, evaporation, entrainment, emulsification, and shoreline interaction of oil. The stochastic and receptor models simulate a large number of trajectories from a single site for generating probability statistics. Each model and the algorithms they use are described. Several additional capabilities are planned for OILMAP, including simulation of tactical spill response and subsurface oil transport. 8 refs
Relaxed memory models: an operational approach
Boudol , Gérard; Petri , Gustavo
2009-01-01
International audience; Memory models define an interface between programs written in some language and their implementation, determining which behaviour the memory (and thus a program) is allowed to have in a given model. A minimal guarantee memory models should provide to the programmer is that well-synchronized, that is, data-race free code has a standard semantics. Traditionally, memory models are defined axiomatically, setting constraints on the order in which memory operations are allow...
Modeling composting kinetics: A review of approaches
Hamelers, H.V.M.
2004-01-01
Composting kinetics modeling is necessary to design and operate composting facilities that comply with strict market demands and tight environmental legislation. Current composting kinetics modeling can be characterized as inductive, i.e. the data are the starting point of the modeling process and
Conformally invariant models: A new approach
International Nuclear Information System (INIS)
Fradkin, E.S.; Palchik, M.Ya.; Zaikin, V.N.
1996-02-01
A pair of mathematical models of quantum field theory in D dimensions is analyzed, particularly, a model of a charged scalar field defined by two generations of secondary fields in the space of even dimensions D>=4 and a model of a neutral scalar field defined by two generations of secondary fields in two-dimensional space. 6 refs
Directory of Open Access Journals (Sweden)
Mosbeh R. Kaloop
2017-01-01
Full Text Available This study evaluates the performance of passively controlled steel frame building under dynamic loads using time series analysis. A novel application is utilized for the time and frequency domains evaluation to analyze the behavior of controlling systems. In addition, the autoregressive moving average (ARMA neural networks are employed to identify the performance of the controller system. Three passive vibration control devices are utilized in this study, namely, tuned mass damper (TMD, tuned liquid damper (TLD, and tuned liquid column damper (TLCD. The results show that the TMD control system is a more reliable controller than TLD and TLCD systems in terms of vibration mitigation. The probabilistic evaluation and identification model showed that the probability analysis and ARMA neural network model are suitable to evaluate and predict the response of coupled building-controller systems.
Directory of Open Access Journals (Sweden)
Luis Gonzaga Baca Ruiz
2016-08-01
Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.
International Nuclear Information System (INIS)
Yao, Ruigen; Pakzad, Shamim N
2014-01-01
In the past few decades many types of structural damage indices based on structural health monitoring signals have been proposed, requiring performance evaluation and comparison studies on these indices in a quantitative manner. One tool to help accomplish this objective is analytical sensitivity analysis, which has been successfully used to evaluate the influences of system operational parameters on observable characteristics in many fields of study. In this paper, the sensitivity expressions of two damage features, namely the Mahalanobis distance of autoregressive coefficients and the Cosh distance of autoregressive spectra, will be derived with respect to both structural damage and measurement noise level. The effectiveness of the proposed methods is illustrated in a numerical case study on a 10-DOF system, where their results are compared with those from direct simulation and theoretical calculation. (paper)
Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation
Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou
2018-06-01
Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.
Brillouin Scattering Spectrum Analysis Based on Auto-Regressive Spectral Estimation
Huang, Mengyun; Li, Wei; Liu, Zhangyun; Cheng, Linghao; Guan, Bai-Ou
2018-03-01
Auto-regressive (AR) spectral estimation technology is proposed to analyze the Brillouin scattering spectrum in Brillouin optical time-domain refelectometry. It shows that AR based method can reliably estimate the Brillouin frequency shift with an accuracy much better than fast Fourier transform (FFT) based methods provided the data length is not too short. It enables about 3 times improvement over FFT at a moderate spatial resolution.
A systemic approach to modelling of radiobiological effects
International Nuclear Information System (INIS)
Obaturov, G.M.
1988-01-01
Basic principles of the systemic approach to modelling of the radiobiological effects at different levels of cell organization have been formulated. The methodology is proposed for theoretical modelling of the effects at these levels
Serpentinization reaction pathways: implications for modeling approach
Energy Technology Data Exchange (ETDEWEB)
Janecky, D.R.
1986-01-01
Experimental seawater-peridotite reaction pathways to form serpentinites at 300/sup 0/C, 500 bars, can be accurately modeled using the EQ3/6 codes in conjunction with thermodynamic and kinetic data from the literature and unpublished compilations. These models provide both confirmation of experimental interpretations and more detailed insight into hydrothermal reaction processes within the oceanic crust. The accuracy of these models depends on careful evaluation of the aqueous speciation model, use of mineral compositions that closely reproduce compositions in the experiments, and definition of realistic reactive components in terms of composition, thermodynamic data, and reaction rates.
Consumer preference models: fuzzy theory approach
Turksen, I. B.; Wilson, I. A.
1993-12-01
Consumer preference models are widely used in new product design, marketing management, pricing and market segmentation. The purpose of this article is to develop and test a fuzzy set preference model which can represent linguistic variables in individual-level models implemented in parallel with existing conjoint models. The potential improvements in market share prediction and predictive validity can substantially improve management decisions about what to make (product design), for whom to make it (market segmentation) and how much to make (market share prediction).
Identification and estimation of non-Gaussian structural vector autoregressions
DEFF Research Database (Denmark)
Lanne, Markku; Meitz, Mika; Saikkonen, Pentti
-Gaussian components is, without any additional restrictions, identified and leads to (essentially) unique impulse responses. We also introduce an identification scheme under which the maximum likelihood estimator of the non-Gaussian SVAR model is consistent and asymptotically normally distributed. As a consequence......, additional economic identifying restrictions can be tested. In an empirical application, we find a negative impact of a contractionary monetary policy shock on financial markets, and clearly reject the commonly employed recursive identifying restrictions....
Automatic Target Recognition Using Nonlinear Autoregressive Neural Networks
2014-03-27
series. Chakraborty et al. (1992) modeled flour prices over an eight year period for the cities of Buffalo, Minneapolis and Kansas City via a neural...on stock and commodity market prices (Kaastra & Boyd, 1996) with a goal of discovering non-linear relationships via ANNs which might provide an...Time Series A vector of past observations from a specific time interval is an example of a time series. For example, monthly stock prices from 2000
A visual approach for modeling spatiotemporal relations
R.L. Guimarães (Rodrigo); C.S.S. Neto; L.F.G. Soares
2008-01-01
htmlabstractTextual programming languages have proven to be difficult to learn and to use effectively for many people. For this sake, visual tools can be useful to abstract the complexity of such textual languages, minimizing the specification efforts. In this paper we present a visual approach for
PRODUCT TRIAL PROCESSING (PTP): A MODEL APPROACH ...
African Journals Online (AJOL)
Admin
This study is a theoretical approach to consumer's processing of product trail, and equally explored ... consumer's first usage experience with a company's brand or product that is most important in determining ... product, what it is really marketing is the expected ..... confidence, thus there is a positive relationship between ...
Development of a Conservative Model Validation Approach for Reliable Analysis
2015-01-01
CIE 2015 August 2-5, 2015, Boston, Massachusetts, USA [DRAFT] DETC2015-46982 DEVELOPMENT OF A CONSERVATIVE MODEL VALIDATION APPROACH FOR RELIABLE...obtain a conservative simulation model for reliable design even with limited experimental data. Very little research has taken into account the...3, the proposed conservative model validation is briefly compared to the conventional model validation approach. Section 4 describes how to account
Comparison of two novel approaches to model fibre reinforced concrete
Radtke, F.K.F.; Simone, A.; Sluys, L.J.
2009-01-01
We present two approaches to model fibre reinforced concrete. In both approaches, discrete fibre distributions and the behaviour of the fibre-matrix interface are explicitly considered. One approach employs the reaction forces from fibre to matrix while the other is based on the partition of unity
Unit root vector autoregression with volatility induced stationarity
DEFF Research Database (Denmark)
Rahbek, Anders; Nielsen, Heino Bohn
We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration to imply non-stationarity. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self...
Unit Root Vector Autoregression with volatility Induced Stationarity
DEFF Research Database (Denmark)
Rahbek, Anders; Nielsen, Heino Bohn
We propose a discrete-time multivariate model where lagged levels of the process enter both the conditional mean and the conditional variance. This way we allow for the empirically observed persistence in time series such as interest rates, often implying unit-roots, while at the same time maintain...... and geometrically ergodic. Interestingly, these conditions include the case of unit roots and a reduced rank structure in the conditional mean, known from linear co-integration to imply non-stationarity. Asymptotic theory of the maximum likelihood estimators for a particular structured case (so-called self...
Modeling thrombin generation: plasma composition based approach.
Brummel-Ziedins, Kathleen E; Everse, Stephen J; Mann, Kenneth G; Orfeo, Thomas
2014-01-01
Thrombin has multiple functions in blood coagulation and its regulation is central to maintaining the balance between hemorrhage and thrombosis. Empirical and computational methods that capture thrombin generation can provide advancements to current clinical screening of the hemostatic balance at the level of the individual. In any individual, procoagulant and anticoagulant factor levels together act to generate a unique coagulation phenotype (net balance) that is reflective of the sum of its developmental, environmental, genetic, nutritional and pharmacological influences. Defining such thrombin phenotypes may provide a means to track disease progression pre-crisis. In this review we briefly describe thrombin function, methods for assessing thrombin dynamics as a phenotypic marker, computationally derived thrombin phenotypes versus determined clinical phenotypes, the boundaries of normal range thrombin generation using plasma composition based approaches and the feasibility of these approaches for predicting risk.
A simple approach to modeling ductile failure.
Energy Technology Data Exchange (ETDEWEB)
Wellman, Gerald William
2012-06-01
Sandia National Laboratories has the need to predict the behavior of structures after the occurrence of an initial failure. In some cases determining the extent of failure, beyond initiation, is required, while in a few cases the initial failure is a design feature used to tailor the subsequent load paths. In either case, the ability to numerically simulate the initiation and propagation of failures is a highly desired capability. This document describes one approach to the simulation of failure initiation and propagation.
A new approach for modeling composite materials
Alcaraz de la Osa, R.; Moreno, F.; Saiz, J. M.
2013-03-01
The increasing use of composite materials is due to their ability to tailor materials for special purposes, with applications evolving day by day. This is why predicting the properties of these systems from their constituents, or phases, has become so important. However, assigning macroscopical optical properties for these materials from the bulk properties of their constituents is not a straightforward task. In this research, we present a spectral analysis of three-dimensional random composite typical nanostructures using an Extension of the Discrete Dipole Approximation (E-DDA code), comparing different approaches and emphasizing the influences of optical properties of constituents and their concentration. In particular, we hypothesize a new approach that preserves the individual nature of the constituents introducing at the same time a variation in the optical properties of each discrete element that is driven by the surrounding medium. The results obtained with this new approach compare more favorably with the experiment than previous ones. We have also applied it to a non-conventional material composed of a metamaterial embedded in a dielectric matrix. Our version of the Discrete Dipole Approximation code, the EDDA code, has been formulated specifically to tackle this kind of problem, including materials with either magnetic and tensor properties.
An Integrated Approach to Modeling Evacuation Behavior
2011-02-01
A spate of recent hurricanes and other natural disasters have drawn a lot of attention to the evacuation decision of individuals. Here we focus on evacuation models that incorporate two economic phenomena that seem to be increasingly important in exp...
Infectious disease modeling a hybrid system approach
Liu, Xinzhi
2017-01-01
This volume presents infectious diseases modeled mathematically, taking seasonality and changes in population behavior into account, using a switched and hybrid systems framework. The scope of coverage includes background on mathematical epidemiology, including classical formulations and results; a motivation for seasonal effects and changes in population behavior, an investigation into term-time forced epidemic models with switching parameters, and a detailed account of several different control strategies. The main goal is to study these models theoretically and to establish conditions under which eradication or persistence of the disease is guaranteed. In doing so, the long-term behavior of the models is determined through mathematical techniques from switched systems theory. Numerical simulations are also given to augment and illustrate the theoretical results and to help study the efficacy of the control schemes.
On Combining Language Models: Oracle Approach
National Research Council Canada - National Science Library
Hacioglu, Kadri; Ward, Wayne
2001-01-01
In this paper, we address the of combining several language models (LMs). We find that simple interpolation methods, like log-linear and linear interpolation, improve the performance but fall short of the performance of an oracle...
Advanced language modeling approaches, case study: Expert search
Hiemstra, Djoerd
2008-01-01
This tutorial gives a clear and detailed overview of advanced language modeling approaches and tools, including the use of document priors, translation models, relevance models, parsimonious models and expectation maximization training. Expert search will be used as a case study to explain the
Approaches to modelling hydrology and ecosystem interactions
Silberstein, Richard P.
2014-05-01
As the pressures of industry, agriculture and mining on groundwater resources increase there is a burgeoning un-met need to be able to capture these multiple, direct and indirect stresses in a formal framework that will enable better assessment of impact scenarios. While there are many catchment hydrological models and there are some models that represent ecological states and change (e.g. FLAMES, Liedloff and Cook, 2007), these have not been linked in any deterministic or substantive way. Without such coupled eco-hydrological models quantitative assessments of impacts from water use intensification on water dependent ecosystems under changing climate are difficult, if not impossible. The concept would include facility for direct and indirect water related stresses that may develop around mining and well operations, climate stresses, such as rainfall and temperature, biological stresses, such as diseases and invasive species, and competition such as encroachment from other competing land uses. Indirect water impacts could be, for example, a change in groundwater conditions has an impact on stream flow regime, and hence aquatic ecosystems. This paper reviews previous work examining models combining ecology and hydrology with a view to developing a conceptual framework linking a biophysically defensable model that combines ecosystem function with hydrology. The objective is to develop a model capable of representing the cumulative impact of multiple stresses on water resources and associated ecosystem function.
Constructing a justice model based on Sen's capability approach
Yüksel, Sevgi; Yuksel, Sevgi
2008-01-01
The thesis provides a possible justice model based on Sen's capability approach. For this goal, we first analyze the general structure of a theory of justice, identifying the main variables and issues. Furthermore, based on Sen (2006) and Kolm (1998), we look at 'transcendental' and 'comparative' approaches to justice and concentrate on the sufficiency condition for the comparative approach. Then, taking Rawls' theory of justice as a starting point, we present how Sen's capability approach em...
Statistical modelling of subdiffusive dynamics in the cytoplasm of living cells: A FARIMA approach
Burnecki, K.; Muszkieta, M.; Sikora, G.; Weron, A.
2012-04-01
Golding and Cox (Phys. Rev. Lett., 96 (2006) 098102) tracked the motion of individual fluorescently labelled mRNA molecules inside live E. coli cells. They found that in the set of 23 trajectories from 3 different experiments, the automatically recognized motion is subdiffusive and published an intriguing microscopy video. Here, we extract the corresponding time series from this video by image segmentation method and present its detailed statistical analysis. We find that this trajectory was not included in the data set already studied and has different statistical properties. It is best fitted by a fractional autoregressive integrated moving average (FARIMA) process with the normal-inverse Gaussian (NIG) noise and the negative memory. In contrast to earlier studies, this shows that the fractional Brownian motion is not the best model for the dynamics documented in this video.
Challenges and opportunities for integrating lake ecosystem modelling approaches
Mooij, Wolf M.; Trolle, Dennis; Jeppesen, Erik; Arhonditsis, George; Belolipetsky, Pavel V.; Chitamwebwa, Deonatus B.R.; Degermendzhy, Andrey G.; DeAngelis, Donald L.; Domis, Lisette N. De Senerpont; Downing, Andrea S.; Elliott, J. Alex; Ruberto, Carlos Ruberto; Gaedke, Ursula; Genova, Svetlana N.; Gulati, Ramesh D.; Hakanson, Lars; Hamilton, David P.; Hipsey, Matthew R.; Hoen, Jochem 't; Hulsmann, Stephan; Los, F. Hans; Makler-Pick, Vardit; Petzoldt, Thomas; Prokopkin, Igor G.; Rinke, Karsten; Schep, Sebastiaan A.; Tominaga, Koji; Van Dam, Anne A.; Van Nes, Egbert H.; Wells, Scott A.; Janse, Jan H.
2010-01-01
A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative
An ontology-based approach for modelling architectural styles
Pahl, Claus; Giesecke, Simon; Hasselbring, Wilhelm
2007-01-01
peer-reviewed The conceptual modelling of software architectures is of central importance for the quality of a software system. A rich modelling language is required to integrate the different aspects of architecture modelling, such as architectural styles, structural and behavioural modelling, into a coherent framework.We propose an ontological approach for architectural style modelling based on description logic as an abstract, meta-level modelling instrument. Architect...
Mathematical modelling a case studies approach
Illner, Reinhard; McCollum, Samantha; Roode, Thea van
2004-01-01
Mathematical modelling is a subject without boundaries. It is the means by which mathematics becomes useful to virtually any subject. Moreover, modelling has been and continues to be a driving force for the development of mathematics itself. This book explains the process of modelling real situations to obtain mathematical problems that can be analyzed, thus solving the original problem. The presentation is in the form of case studies, which are developed much as they would be in true applications. In many cases, an initial model is created, then modified along the way. Some cases are familiar, such as the evaluation of an annuity. Others are unique, such as the fascinating situation in which an engineer, armed only with a slide rule, had 24 hours to compute whether a valve would hold when a temporary rock plug was removed from a water tunnel. Each chapter ends with a set of exercises and some suggestions for class projects. Some projects are extensive, as with the explorations of the predator-prey model; oth...
The simplified models approach to constraining supersymmetry
Energy Technology Data Exchange (ETDEWEB)
Perez, Genessis [Institut fuer Theoretische Physik, Karlsruher Institut fuer Technologie (KIT), Wolfgang-Gaede-Str. 1, 76131 Karlsruhe (Germany); Kulkarni, Suchita [Laboratoire de Physique Subatomique et de Cosmologie, Universite Grenoble Alpes, CNRS IN2P3, 53 Avenue des Martyrs, 38026 Grenoble (France)
2015-07-01
The interpretation of the experimental results at the LHC are model dependent, which implies that the searches provide limited constraints on scenarios such as supersymmetry (SUSY). The Simplified Models Spectra (SMS) framework used by ATLAS and CMS collaborations is useful to overcome this limitation. SMS framework involves a small number of parameters (all the properties are reduced to the mass spectrum, the production cross section and the branching ratio) and hence is more generic than presenting results in terms of soft parameters. In our work, the SMS framework was used to test Natural SUSY (NSUSY) scenario. To accomplish this task, two automated tools (SModelS and Fastlim) were used to decompose the NSUSY parameter space in terms of simplified models and confront the theoretical predictions against the experimental results. The achievement of both, just as the strengths and limitations, are here expressed for the NSUSY scenario.
Lightweight approach to model traceability in a CASE tool
Vileiniskis, Tomas; Skersys, Tomas; Pavalkis, Saulius; Butleris, Rimantas; Butkiene, Rita
2017-07-01
A term "model-driven" is not at all a new buzzword within the ranks of system development community. Nevertheless, the ever increasing complexity of model-driven approaches keeps fueling all kinds of discussions around this paradigm and pushes researchers forward to research and develop new and more effective ways to system development. With the increasing complexity, model traceability, and model management as a whole, becomes indispensable activities of model-driven system development process. The main goal of this paper is to present a conceptual design and implementation of a practical lightweight approach to model traceability in a CASE tool.
Identification of Civil Engineering Structures using Vector ARMA Models
DEFF Research Database (Denmark)
Andersen, P.
The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models.......The dissertation treats the matter of systems identification and modelling of load-bearing constructions using Auto-Regressive Moving Average Vector (ARMAV) models....
New approaches for modeling type Ia supernovae
International Nuclear Information System (INIS)
Zingale, Michael; Almgren, Ann S.; Bell, John B.; Day, Marcus S.; Rendleman, Charles A.; Woosley, Stan
2007-01-01
Type Ia supernovae (SNe Ia) are the largest thermonuclear explosions in the Universe. Their light output can be seen across great distances and has led to the discovery that the expansion rate of the Universe is accelerating. Despite the significance of SNe Ia, there are still a large number of uncertainties in current theoretical models. Computational modeling offers the promise to help answer the outstanding questions. However, even with today's supercomputers, such calculations are extremely challenging because of the wide range of length and timescales. In this paper, we discuss several new algorithms for simulations of SNe Ia and demonstrate some of their successes
Chancroid transmission dynamics: a mathematical modeling approach.
Bhunu, C P; Mushayabasa, S
2011-12-01
Mathematical models have long been used to better understand disease transmission dynamics and how to effectively control them. Here, a chancroid infection model is presented and analyzed. The disease-free equilibrium is shown to be globally asymptotically stable when the reproduction number is less than unity. High levels of treatment are shown to reduce the reproduction number suggesting that treatment has the potential to control chancroid infections in any given community. This result is also supported by numerical simulations which show a decline in chancroid cases whenever the reproduction number is less than unity.
A kinetic approach to magnetospheric modeling
International Nuclear Information System (INIS)
Whipple, E.C. Jr.
1979-01-01
The earth's magnetosphere is caused by the interaction between the flowing solar wind and the earth's magnetic dipole, with the distorted magnetic field in the outer parts of the magnetosphere due to the current systems resulting from this interaction. It is surprising that even the conceptually simple problem of the collisionless interaction of a flowing plasma with a dipole magnetic field has not been solved. A kinetic approach is essential if one is to take into account the dispersion of particles with different energies and pitch angles and the fact that particles on different trajectories have different histories and may come from different sources. Solving the interaction problem involves finding the various types of possible trajectories, populating them with particles appropriately, and then treating the electric and magnetic fields self-consistently with the resulting particle densities and currents. This approach is illustrated by formulating a procedure for solving the collisionless interaction problem on open field lines in the case of a slowly flowing magnetized plasma interacting with a magnetic dipole
A kinetic approach to magnetospheric modeling
Whipple, E. C., Jr.
1979-01-01
The earth's magnetosphere is caused by the interaction between the flowing solar wind and the earth's magnetic dipole, with the distorted magnetic field in the outer parts of the magnetosphere due to the current systems resulting from this interaction. It is surprising that even the conceptually simple problem of the collisionless interaction of a flowing plasma with a dipole magnetic field has not been solved. A kinetic approach is essential if one is to take into account the dispersion of particles with different energies and pitch angles and the fact that particles on different trajectories have different histories and may come from different sources. Solving the interaction problem involves finding the various types of possible trajectories, populating them with particles appropriately, and then treating the electric and magnetic fields self-consistently with the resulting particle densities and currents. This approach is illustrated by formulating a procedure for solving the collisionless interaction problem on open field lines in the case of a slowly flowing magnetized plasma interacting with a magnetic dipole.
Fractal approach to computer-analytical modelling of tree crown
International Nuclear Information System (INIS)
Berezovskaya, F.S.; Karev, G.P.; Kisliuk, O.F.; Khlebopros, R.G.; Tcelniker, Yu.L.
1993-09-01
In this paper we discuss three approaches to the modeling of a tree crown development. These approaches are experimental (i.e. regressive), theoretical (i.e. analytical) and simulation (i.e. computer) modeling. The common assumption of these is that a tree can be regarded as one of the fractal objects which is the collection of semi-similar objects and combines the properties of two- and three-dimensional bodies. We show that a fractal measure of crown can be used as the link between the mathematical models of crown growth and light propagation through canopy. The computer approach gives the possibility to visualize a crown development and to calibrate the model on experimental data. In the paper different stages of the above-mentioned approaches are described. The experimental data for spruce, the description of computer system for modeling and the variant of computer model are presented. (author). 9 refs, 4 figs
A novel approach to modeling atmospheric convection
Goodman, A.
2016-12-01
The inadequate representation of clouds continues to be a large source of uncertainty in the projections from global climate models (GCMs). With continuous advances in computational power, however, the ability for GCMs to explicitly resolve cumulus convection will soon be realized. For this purpose, Jung and Arakawa (2008) proposed the Vector Vorticity Model (VVM), in which vorticity is the predicted variable instead of momentum. This has the advantage of eliminating the pressure gradient force within the framework of an anelastic system. However, the VVM was designed for use on a planar quadrilateral grid, making it unsuitable for implementation in global models discretized on the sphere. Here we have proposed a modification to the VVM where instead the curl of the horizontal vorticity is the primary predicted variable. This allows us to maintain the benefits of the original VVM while working within the constraints of a non-quadrilateral mesh. We found that our proposed model produced results from a warm bubble simulation that were consistent with the VVM. Further improvements that can be made to the VVM are also discussed.
INDIVIDUAL BASED MODELLING APPROACH TO THERMAL ...
Diadromous fish populations in the Pacific Northwest face challenges along their migratory routes from declining habitat quality, harvest, and barriers to longitudinal connectivity. Changes in river temperature regimes are producing an additional challenge for upstream migrating adult salmon and steelhead, species that are sensitive to absolute and cumulative thermal exposure. Adult salmon populations have been shown to utilize cold water patches along migration routes when mainstem river temperatures exceed thermal optimums. We are employing an individual based model (IBM) to explore the costs and benefits of spatially-distributed cold water refugia for adult migrating salmon. Our model, developed in the HexSim platform, is built around a mechanistic behavioral decision tree that drives individual interactions with their spatially explicit simulated environment. Population-scale responses to dynamic thermal regimes, coupled with other stressors such as disease and harvest, become emergent properties of the spatial IBM. Other model outputs include arrival times, species-specific survival rates, body energetic content, and reproductive fitness levels. Here, we discuss the challenges associated with parameterizing an individual based model of salmon and steelhead in a section of the Columbia River. Many rivers and streams in the Pacific Northwest are currently listed as impaired under the Clean Water Act as a result of high summer water temperatures. Adverse effec
A new approach to model mixed hydrates
Czech Academy of Sciences Publication Activity Database
Hielscher, S.; Vinš, Václav; Jäger, A.; Hrubý, Jan; Breitkopf, C.; Span, R.
2018-01-01
Roč. 459, March (2018), s. 170-185 ISSN 0378-3812 R&D Projects: GA ČR(CZ) GA17-08218S Institutional support: RVO:61388998 Keywords : gas hydrate * mixture * modeling Subject RIV: BJ - Thermodynamics Impact factor: 2.473, year: 2016 https://www.sciencedirect.com/science/article/pii/S0378381217304983
Energy and development : A modelling approach
van Ruijven, B.J.|info:eu-repo/dai/nl/304834521
2008-01-01
Rapid economic growth of developing countries like India and China implies that these countries become important actors in the global energy system. Examples of this impact are the present day oil shortages and rapidly increasing emissions of greenhouse gases. Global energy models are used explore
Modeling Approaches for Describing Microbial Population Heterogeneity
DEFF Research Database (Denmark)
Lencastre Fernandes, Rita
environmental conditions. Three cases are presented and discussed in this thesis. Common to all is the use of S. cerevisiae as model organism, and the use of cell size and cell cycle position as single-cell descriptors. The first case focuses on the experimental and mathematical description of a yeast...
Energy and Development. A Modelling Approach
International Nuclear Information System (INIS)
Van Ruijven, B.J.
2008-01-01
Rapid economic growth of developing countries like India and China implies that these countries become important actors in the global energy system. Examples of this impact are the present day oil shortages and rapidly increasing emissions of greenhouse gases. Global energy models are used to explore possible future developments of the global energy system and identify policies to prevent potential problems. Such estimations of future energy use in developing countries are very uncertain. Crucial factors in the future energy use of these regions are electrification, urbanisation and income distribution, issues that are generally not included in present day global energy models. Model simulations in this thesis show that current insight in developments in low-income regions lead to a wide range of expected energy use in 2030 of the residential and transport sectors. This is mainly caused by many different model calibration options that result from the limited data availability for model development and calibration. We developed a method to identify the impact of model calibration uncertainty on future projections. We developed a new model for residential energy use in India, in collaboration with the Indian Institute of Science. Experiments with this model show that the impact of electrification and income distribution is less univocal than often assumed. The use of fuelwood, with related health risks, can decrease rapidly if the income of poor groups increases. However, there is a trade off in terms of CO2 emissions because these groups gain access to electricity and the ownership of appliances increases. Another issue is the potential role of new technologies in developing countries: will they use the opportunities of leapfrogging? We explored the potential role of hydrogen, an energy carrier that might play a central role in a sustainable energy system. We found that hydrogen only plays a role before 2050 under very optimistic assumptions. Regional energy
DEFF Research Database (Denmark)
Bec, Frederique; Rahbek, Anders Christian; Shephard, Neil
2008-01-01
This paper proposes and analyses the autoregressive conditional root (ACR) time-series model. This multivariate dynamic mixture autoregression allows for non-stationary epochs. It proves to be an appealing alternative to existing nonlinear models, e.g. the threshold autoregressive or Markov...... switching class of models, which are commonly used to describe nonlinear dynamics as implied by arbitrage in presence of transaction costs. Simple conditions on the parameters of the ACR process and its innovations are shown to imply geometric ergodicity, stationarity and existence of moments. Furthermore...
Integration models: multicultural and liberal approaches confronted
Janicki, Wojciech
2012-01-01
European societies have been shaped by their Christian past, upsurge of international migration, democratic rule and liberal tradition rooted in religious tolerance. Boosting globalization processes impose new challenges on European societies, striving to protect their diversity. This struggle is especially clearly visible in case of minorities trying to resist melting into mainstream culture. European countries' legal systems and cultural policies respond to these efforts in many ways. Respecting identity politics-driven group rights seems to be the most common approach, resulting in creation of a multicultural society. However, the outcome of respecting group rights may be remarkably contradictory to both individual rights growing out from liberal tradition, and to reinforced concept of integration of immigrants into host societies. The hereby paper discusses identity politics upturn in the context of both individual rights and integration of European societies.
Modelling thermal plume impacts - Kalpakkam approach
International Nuclear Information System (INIS)
Rao, T.S.; Anup Kumar, B.; Narasimhan, S.V.
2002-01-01
A good understanding of temperature patterns in the receiving waters is essential to know the heat dissipation from thermal plumes originating from coastal power plants. The seasonal temperature profiles of the Kalpakkam coast near Madras Atomic Power Station (MAPS) thermal out fall site are determined and analysed. It is observed that the seasonal current reversal in the near shore zone is one of the major mechanisms for the transport of effluents away from the point of mixing. To further refine our understanding of the mixing and dilution processes, it is necessary to numerically simulate the coastal ocean processes by parameterising the key factors concerned. In this paper, we outline the experimental approach to achieve this objective. (author)
Dynamic Metabolic Model Building Based on the Ensemble Modeling Approach
Energy Technology Data Exchange (ETDEWEB)
Liao, James C. [Univ. of California, Los Angeles, CA (United States)
2016-10-01
Ensemble modeling of kinetic systems addresses the challenges of kinetic model construction, with respect to parameter value selection, and still allows for the rich insights possible from kinetic models. This project aimed to show that constructing, implementing, and analyzing such models is a useful tool for the metabolic engineering toolkit, and that they can result in actionable insights from models. Key concepts are developed and deliverable publications and results are presented.
Nuclear security assessment with Markov model approach
International Nuclear Information System (INIS)
Suzuki, Mitsutoshi; Terao, Norichika
2013-01-01
Nuclear security risk assessment with the Markov model based on random event is performed to explore evaluation methodology for physical protection in nuclear facilities. Because the security incidences are initiated by malicious and intentional acts, expert judgment and Bayes updating are used to estimate scenario and initiation likelihood, and it is assumed that the Markov model derived from stochastic process can be applied to incidence sequence. Both an unauthorized intrusion as Design Based Threat (DBT) and a stand-off attack as beyond-DBT are assumed to hypothetical facilities, and performance of physical protection and mitigation and minimization of consequence are investigated to develop the assessment methodology in a semi-quantitative manner. It is shown that cooperation between facility operator and security authority is important to respond to the beyond-DBT incidence. (author)
An Approach for Modeling Supplier Resilience
2016-04-30
interests include resilience modeling of supply chains, reliability engineering, and meta- heuristic optimization. [m.hosseini@ou.edu] Abstract...be availability , or the extent to which the products produced by the supply chain are available for use (measured as a ratio of uptime to total time...of the use of the product). Available systems are important in many industries, particularly in the Department of Defense, where weapons systems
Tumour resistance to cisplatin: a modelling approach
International Nuclear Information System (INIS)
Marcu, L; Bezak, E; Olver, I; Doorn, T van
2005-01-01
Although chemotherapy has revolutionized the treatment of haematological tumours, in many common solid tumours the success has been limited. Some of the reasons for the limitations are: the timing of drug delivery, resistance to the drug, repopulation between cycles of chemotherapy and the lack of complete understanding of the pharmacokinetics and pharmacodynamics of a specific agent. Cisplatin is among the most effective cytotoxic agents used in head and neck cancer treatments. When modelling cisplatin as a single agent, the properties of cisplatin only have to be taken into account, reducing the number of assumptions that are considered in the generalized chemotherapy models. The aim of the present paper is to model the biological effect of cisplatin and to simulate the consequence of cisplatin resistance on tumour control. The 'treated' tumour is a squamous cell carcinoma of the head and neck, previously grown by computer-based Monte Carlo techniques. The model maintained the biological constitution of a tumour through the generation of stem cells, proliferating cells and non-proliferating cells. Cell kinetic parameters (mean cell cycle time, cell loss factor, thymidine labelling index) were also consistent with the literature. A sensitivity study on the contribution of various mechanisms leading to drug resistance is undertaken. To quantify the extent of drug resistance, the cisplatin resistance factor (CRF) is defined as the ratio between the number of surviving cells of the resistant population and the number of surviving cells of the sensitive population, determined after the same treatment time. It is shown that there is a supra-linear dependence of CRF on the percentage of cisplatin-DNA adducts formed, and a sigmoid-like dependence between CRF and the percentage of cells killed in resistant tumours. Drug resistance is shown to be a cumulative process which eventually can overcome tumour regression leading to treatment failure
Tumour resistance to cisplatin: a modelling approach
Energy Technology Data Exchange (ETDEWEB)
Marcu, L [School of Chemistry and Physics, University of Adelaide, North Terrace, SA 5000 (Australia); Bezak, E [School of Chemistry and Physics, University of Adelaide, North Terrace, SA 5000 (Australia); Olver, I [Faculty of Medicine, University of Adelaide, North Terrace, SA 5000 (Australia); Doorn, T van [School of Chemistry and Physics, University of Adelaide, North Terrace, SA 5000 (Australia)
2005-01-07
Although chemotherapy has revolutionized the treatment of haematological tumours, in many common solid tumours the success has been limited. Some of the reasons for the limitations are: the timing of drug delivery, resistance to the drug, repopulation between cycles of chemotherapy and the lack of complete understanding of the pharmacokinetics and pharmacodynamics of a specific agent. Cisplatin is among the most effective cytotoxic agents used in head and neck cancer treatments. When modelling cisplatin as a single agent, the properties of cisplatin only have to be taken into account, reducing the number of assumptions that are considered in the generalized chemotherapy models. The aim of the present paper is to model the biological effect of cisplatin and to simulate the consequence of cisplatin resistance on tumour control. The 'treated' tumour is a squamous cell carcinoma of the head and neck, previously grown by computer-based Monte Carlo techniques. The model maintained the biological constitution of a tumour through the generation of stem cells, proliferating cells and non-proliferating cells. Cell kinetic parameters (mean cell cycle time, cell loss factor, thymidine labelling index) were also consistent with the literature. A sensitivity study on the contribution of various mechanisms leading to drug resistance is undertaken. To quantify the extent of drug resistance, the cisplatin resistance factor (CRF) is defined as the ratio between the number of surviving cells of the resistant population and the number of surviving cells of the sensitive population, determined after the same treatment time. It is shown that there is a supra-linear dependence of CRF on the percentage of cisplatin-DNA adducts formed, and a sigmoid-like dependence between CRF and the percentage of cells killed in resistant tumours. Drug resistance is shown to be a cumulative process which eventually can overcome tumour regression leading to treatment failure.
ISM Approach to Model Offshore Outsourcing Risks
Directory of Open Access Journals (Sweden)
Sunand Kumar
2014-07-01
Full Text Available In an effort to achieve a competitive advantage via cost reductions and improved market responsiveness, organizations are increasingly employing offshore outsourcing as a major component of their supply chain strategies. But as evident from literature number of risks such as Political risk, Risk due to cultural differences, Compliance and regulatory risk, Opportunistic risk and Organization structural risk, which adversely affect the performance of offshore outsourcing in a supply chain network. This also leads to dissatisfaction among different stake holders. The main objective of this paper is to identify and understand the mutual interaction among various risks which affect the performance of offshore outsourcing. To this effect, authors have identified various risks through extant review of literature. From this information, an integrated model using interpretive structural modelling (ISM for risks affecting offshore outsourcing is developed and the structural relationships between these risks are modeled. Further, MICMAC analysis is done to analyze the driving power and dependency of risks which shall be helpful to managers to identify and classify important criterions and to reveal the direct and indirect effects of each criterion on offshore outsourcing. Results show that political risk and risk due to cultural differences are act as strong drivers.
Remote sensing approach to structural modelling
International Nuclear Information System (INIS)
El Ghawaby, M.A.
1989-01-01
Remote sensing techniques are quite dependable tools in investigating geologic problems, specially those related to structural aspects. The Landsat imagery provides discrimination between rock units, detection of large scale structures as folds and faults, as well as small scale fabric elements such as foliation and banding. In order to fulfill the aim of geologic application of remote sensing, some essential surveying maps might be done from images prior to the structural interpretation: land-use, land-form drainage pattern, lithological unit and structural lineament maps. Afterwards, the field verification should lead to interpretation of a comprehensive structural model of the study area to apply for the target problem. To deduce such a model, there are two ways of analysis the interpreter may go through: the direct and the indirect methods. The direct one is needed in cases where the resources or the targets are controlled by an obvious or exposed structural element or pattern. The indirect way is necessary for areas where the target is governed by a complicated structural pattern. Some case histories of structural modelling methods applied successfully for exploration of radioactive minerals, iron deposits and groundwater aquifers in Egypt are presented. The progress in imagery, enhancement and integration of remote sensing data with the other geophysical and geochemical data allow a geologic interpretation to be carried out which become better than that achieved with either of the individual data sets. 9 refs
A moving approach for the Vector Hysteron Model
Energy Technology Data Exchange (ETDEWEB)
Cardelli, E. [Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia (Italy); Faba, A., E-mail: antonio.faba@unipg.it [Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia (Italy); Laudani, A. [Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome (Italy); Quondam Antonio, S. [Department of Engineering, University of Perugia, Via G. Duranti 93, 06125 Perugia (Italy); Riganti Fulginei, F.; Salvini, A. [Department of Engineering, Roma Tre University, Via V. Volterra 62, 00146 Rome (Italy)
2016-04-01
A moving approach for the VHM (Vector Hysteron Model) is here described, to reconstruct both scalar and rotational magnetization of electrical steels with weak anisotropy, such as the non oriented grain Silicon steel. The hysterons distribution is postulated to be function of the magnetization state of the material, in order to overcome the practical limitation of the congruency property of the standard VHM approach. By using this formulation and a suitable accommodation procedure, the results obtained indicate that the model is accurate, in particular in reproducing the experimental behavior approaching to the saturation region, allowing a real improvement respect to the previous approach.
Agribusiness model approach to territorial food development
Directory of Open Access Journals (Sweden)
Murcia Hector Horacio
2011-04-01
Full Text Available
Several research efforts have coordinated the academic program of Agricultural Business Management from the University De La Salle (Bogota D.C., to the design and implementation of a sustainable agribusiness model applied to food development, with territorial projection. Rural development is considered as a process that aims to improve the current capacity and potential of the inhabitant of the sector, which refers not only to production levels and productivity of agricultural items. It takes into account the guidelines of the Organization of the United Nations “Millennium Development Goals” and considered the concept of sustainable food and agriculture development, including food security and nutrition in an integrated interdisciplinary context, with holistic and systemic dimension. Analysis is specified by a model with an emphasis on sustainable agribusiness production chains related to agricultural food items in a specific region. This model was correlated with farm (technical objectives, family (social purposes and community (collective orientations projects. Within this dimension are considered food development concepts and methodologies of Participatory Action Research (PAR. Finally, it addresses the need to link the results to low-income communities, within the concepts of the “new rurality”.
Engineering approach to modeling of piled systems
International Nuclear Information System (INIS)
Coombs, R.F.; Silva, M.A.G. da
1980-01-01
Available methods of analysis of piled systems subjected to dynamic excitation invade areas of mathematics usually beyond the reach of a practising engineer. A simple technique that avoids that conflict is proposed, at least for preliminary studies, and its application, compared with other methods, is shown to be satisfactory. A corrective factor for parameters currently used to represent transmitting boundaries is derived for a finite strip that models an infinite layer. The influence of internal damping on the dynamic stiffness of the layer and on radiation damping is analysed. (Author) [pt
Jackiw-Pi model: A superfield approach
Gupta, Saurabh
2014-12-01
We derive the off-shell nilpotent and absolutely anticommuting Becchi-Rouet-Stora-Tyutin (BRST) as well as anti-BRST transformations s ( a) b corresponding to the Yang-Mills gauge transformations of 3D Jackiw-Pi model by exploiting the "augmented" super-field formalism. We also show that the Curci-Ferrari restriction, which is a hallmark of any non-Abelian 1-form gauge theories, emerges naturally within this formalism and plays an instrumental role in providing the proof of absolute anticommutativity of s ( a) b .
Applied Regression Modeling A Business Approach
Pardoe, Iain
2012-01-01
An applied and concise treatment of statistical regression techniques for business students and professionals who have little or no background in calculusRegression analysis is an invaluable statistical methodology in business settings and is vital to model the relationship between a response variable and one or more predictor variables, as well as the prediction of a response value given values of the predictors. In view of the inherent uncertainty of business processes, such as the volatility of consumer spending and the presence of market uncertainty, business professionals use regression a
Implicit moral evaluations: A multinomial modeling approach.
Cameron, C Daryl; Payne, B Keith; Sinnott-Armstrong, Walter; Scheffer, Julian A; Inzlicht, Michael
2017-01-01
Implicit moral evaluations-i.e., immediate, unintentional assessments of the wrongness of actions or persons-play a central role in supporting moral behavior in everyday life. Yet little research has employed methods that rigorously measure individual differences in implicit moral evaluations. In five experiments, we develop a new sequential priming measure-the Moral Categorization Task-and a multinomial model that decomposes judgment on this task into multiple component processes. These include implicit moral evaluations of moral transgression primes (Unintentional Judgment), accurate moral judgments about target actions (Intentional Judgment), and a directional tendency to judge actions as morally wrong (Response Bias). Speeded response deadlines reduced Intentional Judgment but not Unintentional Judgment (Experiment 1). Unintentional Judgment was stronger toward moral transgression primes than non-moral negative primes (Experiments 2-4). Intentional Judgment was associated with increased error-related negativity, a neurophysiological indicator of behavioral control (Experiment 4). Finally, people who voted for an anti-gay marriage amendment had stronger Unintentional Judgment toward gay marriage primes (Experiment 5). Across Experiments 1-4, implicit moral evaluations converged with moral personality: Unintentional Judgment about wrong primes, but not negative primes, was negatively associated with psychopathic tendencies and positively associated with moral identity and guilt proneness. Theoretical and practical applications of formal modeling for moral psychology are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.
Modeling Saturn's Inner Plasmasphere: Cassini's Closest Approach
Moore, L.; Mendillo, M.
2005-05-01
Ion densities from the three-dimensional Saturn-Thermosphere-Ionosphere-Model (STIM, Moore et al., 2004) are extended above the plasma exobase using the formalism of Pierrard and Lemaire (1996, 1998), which evaluates the balance of gravitational, centrifugal and electric forces on the plasma. The parameter space of low-energy ionospheric contributions to Saturn's plasmasphere is explored by comparing results that span the observed extremes of plasma temperature, 650 K to 1700 K, and a range of velocity distributions, Lorentzian (or Kappa) to Maxwellian. Calculations are made for plasma densities along the path of the Cassini spacecraft's orbital insertion on 1 July 2004. These calculations neglect any ring or satellite sources of plasma, which are most likely minor contributors at 1.3 Saturn radii. Modeled densities will be compared with Cassini measurements as they become available. Moore, L.E., M. Mendillo, I.C.F. Mueller-Wodarg, and D.L. Murr, Icarus, 172, 503-520, 2004. Pierrard, V. and J. Lemaire, J. Geophys. Res., 101, 7923-7934, 1996. Pierrard, V. and J. Lemaire, J. Geophys. Res., 103, 4117, 1998.
Keyring models: An approach to steerability
Miller, Carl A.; Colbeck, Roger; Shi, Yaoyun
2018-02-01
If a measurement is made on one half of a bipartite system, then, conditioned on the outcome, the other half has a new reduced state. If these reduced states defy classical explanation—that is, if shared randomness cannot produce these reduced states for all possible measurements—the bipartite state is said to be steerable. Determining which states are steerable is a challenging problem even for low dimensions. In the case of two-qubit systems, a criterion is known for T-states (that is, those with maximally mixed marginals) under projective measurements. In the current work, we introduce the concept of keyring models—a special class of local hidden state models. When the measurements made correspond to real projectors, these allow us to study steerability beyond T-states. Using keyring models, we completely solve the steering problem for real projective measurements when the state arises from mixing a pure two-qubit state with uniform noise. We also give a partial solution in the case when the uniform noise is replaced by independent depolarizing channels.
Mathematical Modeling in Mathematics Education: Basic Concepts and Approaches
Erbas, Ayhan Kürsat; Kertil, Mahmut; Çetinkaya, Bülent; Çakiroglu, Erdinç; Alacaci, Cengiz; Bas, Sinem
2014-01-01
Mathematical modeling and its role in mathematics education have been receiving increasing attention in Turkey, as in many other countries. The growing body of literature on this topic reveals a variety of approaches to mathematical modeling and related concepts, along with differing perspectives on the use of mathematical modeling in teaching and…
A BEHAVIORAL-APPROACH TO LINEAR EXACT MODELING
ANTOULAS, AC; WILLEMS, JC
1993-01-01
The behavioral approach to system theory provides a parameter-free framework for the study of the general problem of linear exact modeling and recursive modeling. The main contribution of this paper is the solution of the (continuous-time) polynomial-exponential time series modeling problem. Both
A modular approach to numerical human body modeling
Forbes, P.A.; Griotto, G.; Rooij, L. van
2007-01-01
The choice of a human body model for a simulated automotive impact scenario must take into account both accurate model response and computational efficiency as key factors. This study presents a "modular numerical human body modeling" approach which allows the creation of a customized human body
A Bayesian approach for quantification of model uncertainty
International Nuclear Information System (INIS)
Park, Inseok; Amarchinta, Hemanth K.; Grandhi, Ramana V.
2010-01-01
In most engineering problems, more than one model can be created to represent an engineering system's behavior. Uncertainty is inevitably involved in selecting the best model from among the models that are possible. Uncertainty in model selection cannot be ignored, especially when the differences between the predictions of competing models are significant. In this research, a methodology is proposed to quantify model uncertainty using measured differences between experimental data and model outcomes under a Bayesian statistical framework. The adjustment factor approach is used to propagate model uncertainty into prediction of a system response. A nonlinear vibration system is used to demonstrate the processes for implementing the adjustment factor approach. Finally, the methodology is applied on the engineering benefits of a laser peening process, and a confidence band for residual stresses is established to indicate the reliability of model prediction.
A Networks Approach to Modeling Enzymatic Reactions.
Imhof, P
2016-01-01
Modeling enzymatic reactions is a demanding task due to the complexity of the system, the many degrees of freedom involved and the complex, chemical, and conformational transitions associated with the reaction. Consequently, enzymatic reactions are not determined by precisely one reaction pathway. Hence, it is beneficial to obtain a comprehensive picture of possible reaction paths and competing mechanisms. By combining individually generated intermediate states and chemical transition steps a network of such pathways can be constructed. Transition networks are a discretized representation of a potential energy landscape consisting of a multitude of reaction pathways connecting the end states of the reaction. The graph structure of the network allows an easy identification of the energetically most favorable pathways as well as a number of alternative routes. © 2016 Elsevier Inc. All rights reserved.
Carbonate rock depositional models: A microfacies approach
Energy Technology Data Exchange (ETDEWEB)
Carozzi, A.V.
1988-01-01
Carbonate rocks contain more than 50% by weight carbonate minerals such as calcite, dolomite, and siderite. Understanding how these rocks form can lead to more efficient methods of petroleum exploration. Micofacies analysis techniques can be used as a method of predicting models of sedimentation for carbonate rocks. Micofacies in carbonate rocks can be seen clearly only in thin sections under a microscope. This section analysis of carbonate rocks is a tool that can be used to understand depositional environments, diagenetic evolution of carbonate rocks, and the formation of porosity and permeability in carbonate rocks. The use of micofacies analysis techniques is applied to understanding the origin and formation of carbonate ramps, carbonate platforms, and carbonate slopes and basins. This book will be of interest to students and professionals concerned with the disciplines of sedimentary petrology, sedimentology, petroleum geology, and palentology.
Risk prediction model: Statistical and artificial neural network approach
Paiman, Nuur Azreen; Hariri, Azian; Masood, Ibrahim
2017-04-01
Prediction models are increasingly gaining popularity and had been used in numerous areas of studies to complement and fulfilled clinical reasoning and decision making nowadays. The adoption of such models assist physician's decision making, individual's behavior, and consequently improve individual outcomes and the cost-effectiveness of care. The objective of this paper is to reviewed articles related to risk prediction model in order to understand the suitable approach, development and the validation process of risk prediction model. A qualitative review of the aims, methods and significant main outcomes of the nineteen published articles that developed risk prediction models from numerous fields were done. This paper also reviewed on how researchers develop and validate the risk prediction models based on statistical and artificial neural network approach. From the review done, some methodological recommendation in developing and validating the prediction model were highlighted. According to studies that had been done, artificial neural network approached in developing the prediction model were more accurate compared to statistical approach. However currently, only limited published literature discussed on which approach is more accurate for risk prediction model development.
Brajesh Kumar; Singh, Priyanka
2008-01-01
This paper is based on an empirical study of volatility, risk premium and seasonality in risk-return relation of the Indian stock and commodity markets. This investigation is conducted by means of the General Autoregressive Conditional Heteroscedasticity in the mean model (GARCH-in-Mean) introduced by Engle et al. (1987). A systematic approach to model volatility in returns is presented. Volatility clustering and asymmetric nature is examined for Indian stock and commodity markets. The risk-r...
A dual model approach to ground water recovery trench design
International Nuclear Information System (INIS)
Clodfelter, C.L.; Crouch, M.S.
1992-01-01
The design of trenches for contaminated ground water recovery must consider several variables. This paper presents a dual-model approach for effectively recovering contaminated ground water migrating toward a trench by advection. The approach involves an analytical model to determine the vertical influence of the trench and a numerical flow model to determine the capture zone within the trench and the surrounding aquifer. The analytical model is utilized by varying trench dimensions and head values to design a trench which meets the remediation criteria. The numerical flow model is utilized to select the type of backfill and location of sumps within the trench. The dual-model approach can be used to design a recovery trench which effectively captures advective migration of contaminants in the vertical and horizontal planes